Systems and methods for providing recommendations based on collaborative and/or content-based nodal interrelationships

ABSTRACT

In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is continuation-in-part of U.S. application Ser.No. 14/537,319, filed Nov. 10, 2014, which is a continuation of U.S.application Ser. No. 14/267,464, filed May 1, 2014, now U.S. Pat. No.8,909,583, which is a continuation of U.S. application Ser. No.13/919,301, filed Jun. 17, 2013, now U.S. Pat. No. 8,756,187, which is acontinuation of U.S. application Ser. No. 13/416,945, filed Mar. 9,2012, now U.S. Pat. No. 8,515,893, the entire contents of each of whichare incorporated herein by reference.

BACKGROUND

Search engines may output lists of hyperlinks for web pages that includeinformation of interest. Some search engines base the determination ofcorresponding hyperlinks on a search query entered by the user. The goalof the search engine is to return links for high quality, relevant sitesbased on the search query. Most commonly, search engines accomplish thisby matching the terms in the search query to a database of stored webpages or web page content. Web pages that include the terms in thesearch query are considered “hits” and are included in the list ofhyperlinks presented to the user.

To increase efficacy of the search, a search engine may rank the list ofhits or hyperlinks according to the relevance or quality. For example,the search engine may assign a grade or rank to each hit, and the scoremay be assigned to correspond to the relevance or importance of the webpage. Conventional methods of determining importance or relevance arebased on the content of each web page including the link structure ofthe web page.

Many conventional search engines utilize an indexing system foridentifying web pages available on the Internet. The indexing systemidentifies words in the pages and creates an index of those words. Thesystem responds to user queries by analyzing the index and identifyingthe pages that are most relevant to the users query.

The relevance ranking or determination can be executed in various ways.The citation of one site or page by other sites or pages is sometimesused as one measure of relevance. Web page metadata is also sometimesused in a determination of relevance.

Neural networks have also been used in the field of Internet searching.It is assumed, for purposes of this description, that the reader isfamiliar with how neural networks operate. A neural network can consistof three basic aspects—a neuron or node, definitions of how the neuronsor nodes are interconnected or related to each other, and the manner inwhich that topology is updated over time.

SUMMARY

In selected embodiments a recommendation generator builds a network ofinterrelationships among venues, reviewers and users based on theirattributes and reviewer and user reviews of the venues. Eachinterrelationship or link may be positive or negative and may accumulatewith other links (or anti-links) to provide nodal links the strength ofwhich are based on commonality of attributes among the linked nodesand/or common preferences that one node, such as a reviewer, expressesfor other nodes, such as venues. The links may be first order (based ona direct relationship between, for instance, a reviewer and a venue) orhigher order (based on, for instance, the fact that two venue are bothliked by a given reviewer). The recommendation engine in certainembodiments determines recommended venues based on user attributes andvenue preferences by aggregating the link matrices and determining thevenues which are most strongly coupled to the user. The systemarchitecture in various embodiments may permit efficient, localizedupdating of the neural network in response to alteration of theattributes of various nodes. In selected embodiments a recommendationgenerator builds a network of interrelationships between venues,reviewers and users based on their attributes and reviewer and userreviews of the venues which are enhanced by dynamic resonance betweensource sites. The recommendation engine in certain embodimentsdetermines recommended venues based on user attributes and venuepreferences by performing geometric contextualization on generatedrecommendation sets and determining recommendation resonance with pastrecommendations. Remote businesses may also link with the recommendationgenerator to receive recommendations custom-tailored to their business.In selected embodiments, interconnectivity augmentation provides forenhanced neural network topology and recommendations for foreignlocales. Various user interfaces are also contemplated thereby providingusers with a view of the neural network topology as well as the abilityto collaboratively determine meeting places.

The details of one or more implementations are set forth in theaccompanying drawing and description below. Other features, objects, andadvantages will be apparent from the description and drawings, and fromthe claims.

BRIEF DESCRIPTION OF FIGURES

FIG. 1A is a block diagram of an environment for developing andutilizing a network of interrelated nodes.

FIG. 1B is a diagram of a process flow executed by an exemplary contentcollection system.

FIG. 1C is a diagram of a process flow executed by an exemplary contentorganization system.

FIG. 2 is a diagram showing the interrelationships between venues,reviewers and users.

FIG. 3 is chart including reviewer ratings according to one example.

FIG. 4 is a chart including venue attributes according to one example.

FIG. 5 is a chart including reviewer attributes according to one example

FIG. 6 is a chart including user attributes according to one example.

FIGS. 7A and 7B show a matrix of content-based venue links according toone example.

FIGS. 8A and 8B show a matrix of collaborative venue link according toone example.

FIG. 9 is a chart illustrating a recommendation generation according toone example.

FIG. 10 is a chart illustrating a connection grown according to oneexample.

FIG. 11 is a chart illustrating pre-normalization matrix data accordingto a second example.

FIG. 12 is a chart illustrating post-normalization matrix data accordingto a second example.

FIG. 13 is a chart illustrating connection creep according to a secondexample.

FIG. 14 is a user interface according to one example.

FIG. 15 illustrates a topographical world view user interface displayingthe neural network topology according to one example.

FIG. 16 illustrates a topographical local view user interface displayingthe neural network topology according to one example.

FIG. 17 illustrates a collaborative decision making user interfaceaccording to one example.

FIG. 18 is a chart illustrating the data repository of previousrecommendations served to users according to one example.

FIG. 19 is a chart illustrating aggregate data repository recommendationdata according to one example.

FIG. 20 is a flow chart illustrating error correction and dataverification processing according to one example.

FIGS. 21A-21D illustrate recommendation generation and recommended datavalues after geometric contextualization according to one example.

FIG. 22 is a flow chart illustrating geometric contextualizationprocessing according to one example.

FIG. 23 is a chart illustrating reviewer ratings between differentlocales according to one example.

FIG. 24 shows a matrix of collaborative venue links based on thereviewer ratings illustrated in FIG. 23 according to one example.

FIG. 25 illustrates inter nodal connections after interconnectivityaugmentation processing according to one example.

FIG. 26A is a chart illustrating venue attributes according to oneexample.

FIG. 26B is a chart illustrating congruency factors determined viainterconnectivity augmentation according to one example.

FIG. 27 illustrates inter nodal connections after interconnectivityaugmentation processing according to one example.

FIG. 28 illustrates an exemplary interaction via the system API betweenthe server and a plurality of merchants.

Like reference symbols in various drawing indicate like elements.

DETAILED DESCRIPTION OF ILLUSTRATIVE IMPLEMENTATIONS Overview ofSelected Embodiments

In certain implementations a recommendation engine may generaterecommendations based on attributes and data associated with venues,users, reviewers and reviews. The system may harvest reviews generatedby various reviewing entities parse those reviews into an organizeddatabase of review data. That data may include attributes of the venue(such as a restaurant) and the rating or assessment provided by thereviewer. The system may also gather or generate data concerning theattributes of reviewer, such as gender, age, profession, marital status,review frequency and review accuracy. The system, in one implementation,also gathers data concerning the attributes of user, such as gender,age, profession, marital status, and affinity (whether positive ornegative) for certain venues.

The exemplary system may generate a neural network of interrelationshipsbased on venue attributes and reviewer attributes. For instance, venuesmay be linked by common features such as price, genre, attire, location,or affinity expressed by the same reviewer. Reviewers may be linked bypersonal characteristics or common affinities for certain venues.Reviewers and venues may be linked by common attributes of reviewerswith a given affinity for a specific venue or common venue attributesfor venues liked by a given reviewer.

The system may create interrelationships between and amongst venues andreviewers of different species. For instance, interrelated venues mayinclude restaurants, theaters, events and institutions. Interrelatedreviewers may include periodicals and individual reviewers.

Each link may incrementally strengthen or weaken the overallinterrelationship between two venues, a venue and a reviewer, or tworeviewers. Each link may affect neighboring links, either by causing theneighboring links to strengthen or weaken based on the magnitude of theorigin link. When two reference nodes (e.g. venues) are each connect toa common node (e.g., a venue), the system can generate an additionallink or interrelationship between the two reference nodes.

The interrelationships can be broadly categorized as collaborative andcontent-based. Collaborative relationships are a function of affinitiesexpressed by a given reviewer.

Stated another way, collaborative links are usually between things agiven user likes, often irrespective of why the user likes them.Content-based relationships are a function of the features held incommon among venues in a given subset. Stated another way, content-basedlinks are usually between things within a group which have commonfeatures. Hybrids of these approaches may also be used, for example, alink may identify venues among those liked by a given reviewer whichhave features in common.

The neural network of interrelationships grows dynamically as furtherreview, reviewer and venue data is added. The system may continuouslyanalyze the data to add positive or negative collaborative links,content links, or content-collaborative links. The system may create newderivative links, normalize the data to adjust for data skew, and adjustlinks based on neighboring link values.

In various implementations the system may generate recommendations basedon user attributes and data associated with a recommendation request.The system may provide a plurality of recommendations based overall linkstrengths that factor in collaborative and content-basedinterrelationships. The recommendations may include venues complementaryto that specifically requested, for instance, in response to a userrequest for a restaurant recommendation the system may generate atheater or night club recommendation as well.

Exemplary System Architecture

FIG. 1 illustrates an exemplary network architecture for a server-basedrecommendation generation system 100. It will be understood that some orall of the functionality described herein may be relocated to a clientdevice application (such as a smart phone application) based on theclient device's communication, data storage and computationalcapabilities.

The server 102 hosts a plurality of engines and modules. In thisapplication the user interface module 110 resides on the server 102 andserves web pages or suitable content to a client side application. Thecrawl and parsing module 114 executes the web crawling and source datacollection operations described below. The recommendation engine 112accesses the matrices of interrelationships and generates therecommendations according to the techniques described herein. Themerchant interface provides the functionality describe below concerningvenue operators' interaction with the server and accessing projectionsand reports generated thereby.

The data repository 118 stores the matrices of interrelationships. Therepository includes a matrix builder 126 which builds the datastructures reflecting the nodal interrelationships based on review data122 which is collected from review sites 106 by the crawl and parsingmodule 114. The matrix builder also incorporates venue, reviewer anduser data 124 collected from users 108, venues 104 and other web pages(by the crawl and parsing module 114).

The network 120 includes in certain embodiments the Internet orworld-wide web. The network may also comprise proprietary andsemi-propriety networks such as cellular data networks, intranets, VPNs,or extranets.

Those skilled in the art will understand that the techniques describedherein may be implemented in various system and database topologies andconsistent with various computational methodologies. Topologies andmethodologies suitable for aspects of various embodiments are describedin K. R. Nichols, A Reconfigurable Computing Architecture forImplementing Artificial Neural Networks on FPGA, Master's Thesis, TheUniversity of Guelph, December 2003; F. Rosenblati, The Perception: AProbabilistic Model For Information Storage And Organization In TheBrain, Psychol!, Rev., 65(6):386-408, 1958; K. Steinbuch and U. A. W.Piske; Learning Matrices and their Applications. IEEE Trans. Electron.Computers; 12:846-862, 1963; J. A Bamden, High-level Reasoning,Computational Challenges for Connectionism, and the Composite solution.Appl. Intell., 5(2):103-135, April 1995; B. Denby, P. Garcia, B.Granado, C. Kiesling, J. C. Prevotet and A. Wassatch, Fast Triggering inHigh Energy Physics Experiments Using Hardware Neural Networks, IEEETrans. On Neural Networks, 14(5):1010-1027, September 2003; R. O. Duda,P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley & Sons,New York, 2nd edition, 2001; H. Eichenbaum, The Cognitive Neuroscienceof Memory: An Introduction, Oxford University Press, New York, 2002; K.Fukushima, Cognitron: A Self-Organizing Multilayered Neural Network,Biol. Cybern, 20(3-4): 127-136, 5 Nov. 1975; K. Fukushima and S. Miyake.A Self-Organizing Neural Network With A Function Of Associative Memory:Feedback Type Cognitron, Biol. Cybern., 28(4):201-208, 3 Mar. 1978; J.M. Fuster. Cortex and Mind: Unifying Cognition. Oxford University Press,New York, 2002; R. Gadea, J. Cerda, F. Ballesterand A. Mocholi,Artificial Neural Network Implementation On A Single FPGA Of A PipelinedOn-Line Backpropagation, ISSS 2000, Madrid, Spain, September 2000; S.Grossberg, Adaptive Pattern Classification And Universal Recoding: I.Parallel Development And Coding Of Neural Feature Detectors. Biol.Cybern., 23(3):121-134, 30 Jul. 1976; S. Grossberg, Adaptive PatternClassification And Universal Recoding: IL Feedback, Expectation,Olfaction, Illusions, Biol. Cybern., 23(4):187-202, 30 Aug. 1976; S.Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall,Upper Saddle River, N.J., 2nd edition, 1999; R. Hecht-Nielsen,Neurocomputing, Addison Wesley, Reading, Mass., 1989; R. Hecht-Nielsen,A Theory Of Thalamocortex, in R. Hecht-Nielsen and T. McKenna, editors,Computational Model!. for Neuroscience: Human Cortical Information; S.Y. Kung, M. W. and S. H. Lin., Biometric Authentication: A MachineLearning Approach. Prentice Hall PTR, Upper Saddle River, N.J., 2005; B.Widrow and M. Kamenetsky, On The Efficiency Of Adaptive Algorithms, InS. Haykin and B. Widrow, editors, Least-Mean-Square Adaptive Filters,John Wiley & Sons, New York, 2003; B. Widrow and M. Kamenetsky,Statistical Efficiency Of Adaptive Algorithms, Neural Netw.,16(5-6):735-744, June-July 2003; B. Widrow and M. A. Lehr, 30 Years OfAdaptive Neural Networks: Perception, Madaline, and backpropagation,Proc. IEEE, 78(9):1415-1442, September 1990; U.S. Pat. No. 7,840,569,entitled “Enterprise relevancy ranking using a neural network,” which isincorporated herein by reference; U.S. Pat. No. 7,895,140, entitled“Neural Network Learning Device, Method, And Program,” which isincorporated herein by reference; and U.S. Pat. No. 7,979,370, entitled“Neural Network For Electronic Search Applications,” which isincorporated herein by reference.

Node/Venue Types

The nodes in the neural network in one implementation are venues such asrestaurants, theaters, night clubs, hotels, concerts and other events.However, due to the flexibility of the systems and methodologiesdescribed herein they may be applied in a variety of other manners.Nodes in the network may be sub-venue items such as specific mend itemsor specific rooms inside a hotel. The nodes may also be styleconsumables such as clothing, furniture or wine or rather content suchas music, books, magazines, TV shows, or movies. The nodes areoptionally set to be services such as mechanics, barbers,transportation, doctors, dentists, landscape architects, interiordesigners, or nanny services. In other implementations the nodes mayneighborhoods or cities in which to live, colleges to apply to, careersthat are a good fit, or grocery stores. In still other applications thenodes may be associated with social aspects such as friends andactivities the user might like. The nodes in other embodiments aremedical conditions or treatments.

The techniques described herein may also be used for fraud detection byproviding predictions of what a user is unlikely to do, which in turn ismore likely to be associated with fraudulent use of a credit card (forinstance). The techniques may also be used for marketing/co-brandingopportunities by predicting brand affinity even across disparatecategories. The techniques may also be applied to actuarial/riskassessment applications by analyzing co-occurrences between a user'sfine-scale likes and dislikes, which can be utilized as indicators ofrisk. The techniques may also be used to predict financial marketbehavior or trends by aggregating markets into “group users” andpredicting behavior of that group user as described hereinbelow. In asimilar vein predictions on mass human behavior can be achieved withrespect to geographic movement (migratory patterns) and thereby censusand demographic projections over time may be generated for use byretailers, real estate developers, and others. Moreover, the techniquesmay be used to gauge affinity for certain types of media (such atelevision shows) or media channels (cable or web).

As will be appreciated from the following description, in each suchimplementation the nodal attributes, reviewer attributes and theinterrelationships will be selected to correspond in part to the factorswhich are causally associated with reviewer's preferences for certainnodes. For instance, in a system designed to provide career suggestionsthe nodal attributes may includes skills associated with each professionand user attributes may include aptitude scores or survey questionnaireresults.

Hereinbelow the system 100 is described in connection with exemplarysystems in which the nodes are venues such as restaurants, hotels ortheaters. For convenience the term “venue” is used to refer to neuralnetwork nodes. It should be understood that the term “venue” in thefollowing sections is used broadly to refer to any entity or item thatis interrelated in the network with other network nodes such as usersand/or reviewers.

Identification of Venue Reviews

A user's or reviewer's affinity (again, positive or negative) for avenue is derived from both evaluations and assessments of venues, suchas reviews or ratings, and implicit data sources such as ant trails.Individuals may publish ratings on social webpages, review forums andwebsites or blogs. Ratings may also be published by votes placed via“Like” or “Ding”' buttons disposed on various websites. As one example,user reviews of restaurants can be found at menuism.com, dine.com,opentable.com, google.com, reviewsahoy.com, and realeats.com. Anindividual's affinity for certain venues can also be discerned fromtheir spending habits or purchase history, data of which can be gleanedfrom financial transaction records such as credit card statements. Anindividual's web browsing history or ant trail can also provide insightinto affinity for certain venues, as discerned from cookies or thevarious reviews an individual generates across multiple forums,including but not limited to websites associated with each venue. Anindividual's website navigation bookmarks and browsing history alsoreflect browsing behavior and may likewise be mined for source data. Thegeographic position of an individual over time, such as derived fromcellular GPS data, can likewise be correlated with venues and therebygenerate data reflective of venue affinity. This approach may providedwell time data as well, which can be used to sort or arrange the data.Magazine subscriptions information may also be used as indicators of anindividual's affinity for given venues (as that term is broadly usedherein). An individual's professional licenses can also be used as datasources for affinity for venues, including but not limited toorganizations.

The foregoing sources of data concerning venue affinity can beprioritized based on factors germane to the strength of the correlationbetween the data and the affinity of interest. Data or sites that referto a greater number of venues might be more probative since such sitesare more likely to compare, contrast or rank venues. Similarly, sitesthat specify a greater number of properties, such as in structuredfields, for each venue or reviewer tend to be more effective orprobative. Sites with a greater number of reviews per venue and/orreviews per reviewer are, on balance, to include more reliable affinity,The inclusion of “related items,” “also viewed,” or “people whopurchased this also purchased” fields or boxes can also be considered asindicators that the site's data will be strongly correlated to actualaffinities. In a similar vein, a site's inclusion of geographicallyproximate recommendations, recommendations based on social networking,and recommendations based of complementary venues (e.g. hotel andrestaurant) may be indicative of more reliable data.

The behavior of the more effective or accurate reviewers also can beanalyzed to differentiate various data sources, for example, bydetermining where those reviewers tend to post reviews. The existence ofgrouping structures, such as data structures associated with a pluralityof socially networked individuals, can also be used as a metric to gradeor rate the potential value of the site's data. Blogs may also becrawled to determine which reviews or ratings sites are the mostcommonly referenced.

In one embodiment, numeric values are associated with some or all of theforegoing variables and weights are assigned to each variable based onthe system designer's estimation of the relative strength of correlationbetween the variable and the predictive value of the review data on thesite. For instance, the density of the best reviewers on a site may beweighted more heavily than the number of venues referenced on a site.The resulted weighted numerical grades can be used to prioritizeharvesting operations.

Harvesting Venue Reviews and Reviewer Data

The reviews may be harvested using web crawling techniques such as thosedescribed in U.S. Pat. No. 6,631,369, entitled “Method and System forIncremental Web Crawling” and assigned to IBM Corporation, which isincorporated herein by reference. According to that technique, in aninitial crawl, the crawler creates a first full index for the documentstore after which incremental crawls are executed.

Alternatively or in addition, the system 100 may target cached web pagesserved by commercial search engines. A suitable protocol for rebuildingcontent sites from search engine caches is as follows. First, a completevenue listing for a category by crawling a directory such as a YellowPages or other suitable directory. For each item in the directory, thesystem 100 runs a series of search queries in various search engines,each query restricted to results for the content site of interest, suchas dine.com. The search results are parsed and the URLs for the relevantcached pages are retrieved. The cached pages are then retrieved and in arepository, after which they are parsed based on the name, city, phonenumber, and other data fields associated with a venue of interest. Inthis manner the cached review page for the venue of interest may beidentified. This process is optionally repeated across search enginesand across multiple venues, targeting the sites prioritized as set forthin the preceding section, to collect the desired array of source data.

The data may optionally be validated by checking parsed venue orreviewer content for blank fields. Venue or reviewer content may also bechecked against unique identification information (a venue phone numberor a reviewer email address or screen name) to ensure sure that itcorresponds to the target venue or reviewer.

After validation, the pages may be parsed to extract the data ofinterest. Parser code may be used to segregate out the structured fieldsof interest, the reviews, and other information of interest as describedabove. The extracted data may be upload the data in database tables orfiles to be analyzed for computing personalization. Techniques such, asthose taught in U.S. Pat. No. 7,788,293, entitled “Generating StructuredInformation” assigned to Google Inc., the contents of which are hereinincorporated by reference, may be used for this purpose.

The same approaches can be used to harvest data concerning reviewers orusers (discussed in more detail below). The data is preferentially in astructured format on a public site and is predictive of personality andaffinities. The data sources may be prioritized or ranked as set forthin the preceding section, such as according to the number of reviews,given by the reviewer, the citation of a reviewer's reviews on othersites and the alignment of a reviewer's reviews with overall ratingsgenerated by the system 100 (as discussed below) and third party reviewsites from which data is harvested. The reviewer data is thenselectively crawled and parsed as explained above.

The crawl and parser module 114 may be configured to coordinate thecrawling and digestion of certain web or network nodes. Due to practicallimitations the entire World Wide Web cannot be crawled and parsedsimultaneously. The crawling and parsing process may be coordinatedacross different content-gathering computers or agents. Multiple remotecrawling engines (at remote network nodes) may be deployed, each ofwhich can check data sources (such as web pages or cached web pages) forthe properties described above and recruit crawling and parsing nodes inthe event rich data sources are located. The remote crawling nodes cancoordinate their crawling based on real-time breaking news events, oroptimize content gathering in response to shifts in mass user behavioras reflected in the data matrices described herein.

Examples of content collection and content organization systems andprocess flows are shown in FIGS. 1B and 1C. FIG. 1B illustrates theprocess executed by the content collection system, which may include thecrawl and parsing module 114. At box 150 the crawl and parsing module114 identifies subject matter targets, such as rock-climbing, are neededin the neural network. The targets may also take the form of specificURLs or collections thereof. At box 152 the module 114 identifies thecurrent content, in the form of previously collected web pages (orrepresentations thereof), that already resides within the system'sstorage network. At step 154 the content collector, which in oneembodiment takes the form of a persistent system network node,determines from a comparison and analysis of the two inputs whichsubject matter or URLs are to be gathered by the module 114. The contentcollector verifies the addresses and content of the target sitescontaining the subject matter which is to be collected and creates aqueue of items to be crawled and parsed by the module 114. As anexample, the distributed queue's first entry might be [Boston,restaurants, google.com, ‘air] which corresponds to a request that thecrawler nodes collect all cached pages associated with google.com'sreviews of any Boston area restaurant. The content collector may alsodynamically allocate certain queue items to specific crawling nodesbased on their relative priority (160). At step 162 the contentcollection engine, which includes a distributed array of crawler nodes,receives or access the distributed queue and dynamically assignedcollection commands from the content collector. The content collectionengine, under the control of crawl and parsing module 114, collectscached web pages as discussed above. The output is a library of cachedweb content which is parsed according to the methods described herein.

FIG. 1C shows an exemplary process executed by the content organizer,which may comprise the matrix builder 116. At step 174 the contentorganizer receives or accesses the library of cached pages to be parsedand added to the network. The content organizer may be a persistentsystem network node in various embodiments. The content organizationengine (see step 182) may include a distributed array of parsing nodesthat access a distributed queue of parsing assignments and receiveassignments which are dynamically assigned, optionally to specificcrawling nodes or crawling nodes having certain attributes such asbandwidth or throughput. The content organization engine also accessesan array of site-specific parsers which are specially designed to parsedata as it is presented on certain sites. For instance, becauseGoogle.com may present its hotel data in a format different thanrestaurants, a parser engine specific to Google's hotel pages ispresented to the content organization engine for use in parsingcorresponding cached web pages. Other examples, as shown in FIG. 1Cinclude a parser specific to Facebook.com's venue or event pages. Thisarchitecture may facilitate modification of parser engines as sitesalter the manner in which they present data. For example,Local.yahoo.com may alter the data format of its hotel pages, inresponse to which a single parser engine can be updated. The output ofthe content organization engine (182) is used by the matrix builder 114to create additional nodes and matrices of interrelationships asdescribed herein. The resulting matrices and databases of web contentare presented for simultaneous access by multiple instances of webservers which present the user interface described below or whichcommunicate with mobile device client applications as discussed herein.

Collection of User Data

Upon creation of an account or in response to another triggering eventsuch as a request for a new recommendation the system 100 may require auser to input various data including gender, age, marital status,children ages, children gender, third parties with whom the user issocially networked, hobbies, interests, favorite venue information (inone or more venue categories), preferred or non-preferred reviewingentities (if any).

The user is then asked to list favorite or preferred venues. As anexample, the user may list favorite restaurants. The system 100 asks foralternative favorites in the event the restaurant is not included withinthe neural network.

The system 100 optionally may crawl the web for additional informationconcerning the user and then parse and validate the data according tothe methods described above. This supplemental data may be added to theuser's profile, data from which will be used in various operations asset forth below.

Creating Nodal Interrelationships

Nodes in the data network represent venues, venue properties, users,user properties, reviewers, reviewer properties, and the like. Links orlinks represent relations between those nodes. The number of linksbetween two items might therefore grow as data on two items grows. Thestrength of each link denotes the affinity between the two connecteditems, such as similarity of star rating (in a review of a venue),number of attributes held in common. Links can be either positive ornegative in sign.

Links can be associated to designate affinity between and amongst,venues, properties of venues, users, reviewers, content sources, or anycombination thereof. For instance, as shown in FIG. 2, two venues 200,210 may be interrelated in that they have several attributes 201, 211 incommon, namely that they are both Italian restaurants in the sameneighborhood. Reviewers 220, 230 are related in that they likewise havemultiple attributed in common. Users 240, 250 are likewise interrelatedby shared attributes. Reviewer 220 is interrelated with both venues 200and 210 in that Reviewer delivered a review to both venues and that inturn creates an additional relationship between venues 200 and 210(namely, they were reviewed by the same reviewer. User 250 is related toboth Reviewers 220 and 230 via shared attributes and User 240 is relatedonly to Reviewer 220 via the shared attributes. Reviewers 220 and 230are thus interrelated also in that they share attributes of user 240.User 240 is also directly linked to venue 200 by virtue of the fact thatthe user has expressed an affinity for that specific venue. Reviewers220 and 230 thus have a second order relationship with venue 200 throughuser 240.

This data architecture permits links, or interrelationships, to beadjusted independently from one another. Links touching the same nodecan be adjusted for one partner node but not others. Links on the samenode can be “scaled” together to maintain relative values of each oftheir partners while changing the overall drive/influence to that node.

In selected embodiments, subtractive or “anti-related” links can weakenrelationships from one node onto another. Subtractive nodes also can beadded to the network to normalize the total positive timbre of localnodes where the average link values are too strongly positive.Subtractive nodes also can serve to mediate competition between nodes toinfluence one another, as the strength of the link dictates the effectone node will have on the other. Subtractive nodes can help sharpen, orfocus, the positive influence cast by a given node.

Links can in various implementations be sorted according to priority ofinfluence over (or strength of link to) their downstream node. Links mayinteract and influence one another, where the addition of one changesthe strength or presence of another, in a manner that is restricted ortargeted to other links on the same node.

Links from reviewer nodes can be normalized based on how positive ornegative they are. In other words, if a given reviewer is an “easygrader” his or her reviews may be lessened in magnitude to normalize thereviews to a statistic goal or mean. Links from reviewer nodes may alsobe normalized to lessen the influence of those links where, forinstance, a reviewer has an extraordinarily high number of reviews (eachof which creates a link) and thus that single reviewer's opinion wouldunduly influence the data network if not scaled appropriately.Conversely, the strength of a reviewer link make by scaled upwards basedon measured or perceived effectiveness or accuracy of the reviewer. Thismay be executed, for instance, through rankings or ratings of reviewersor statistical feedback whereby accuracy or predictiveness of reviewersis measured.

Weighting or normalization may also be used to alter a link's strengthbased on the number of attributes in held in common. For instance, thesystem 100 may be configured to give each additional link of a giventype a linearly or exponentially decreasing affect, such as where asubstantial number of interrelated reviewers given a venue a similarreview. Links between nodes which are hyper-connected may be likewise bescaled downward to reduce the effect that one of the two nodes has onthe extended network. The converse—giving cumulative links escalatingeffect or increasing link strength for under-connected nodes—may also beimplemented with the opposite effects.

Links may also be weighted based on the predictiveness of the reviewer.For instance, reviewers may be graded based on number of reviews, numberof citations on other web sites, or ratings of reviewers on third partysites crawled by the system. The links created based on each reviewer'sreviews may accordingly be scaled linearly or non-linearly according tothe relative grade of the reviewer. Reviews provided by more highlyrated reviewers may be assigned correspondingly higher values orstrengths.

Reviewers may be weighted on a user-specific basis as well. For example,the neural network of links may be reweighted based on the fact that theuser requesting a recommendation has affinities or attributes held incommon with certain reviewers. Reviewers' ratings may be correspondingweighted more heavily or more lightly in correspondence to the linkbetween the user and the various reviewers.

Reviewers may optionally be pruned from the network if they have below athreshold level of relevance as measured by a corresponding grade oreffectiveness. As noted elsewhere herein, the grades of reviewers may bebased on ratings of reviewers at third party sites and/or feedback ofusers of the system 100 concerning agreement or disagreement withrecommendations which were calculated in part based on a givenreviewer's review. If a reviewer is pruned from the system the remainingreviewer's weightings may be adjusted upwards to maintain normalization.

The links in the neural network may be bidirectional (as shown in thefigures) or unidirectional. In certain circumstances, the predictivenessof a link may be asymmetrical or unidirectional. For example, it may bethe case that almost everyone who likes restaurant A likes restaurant B,but very few who like restaurant B also like restaurant A. In that casethe links associated with affinity for restaurant. A mayunidirectionally point to (be linked to) restaurant B but the conversewould not be true—node B would not have a positive link to restaurant Abased on this data point. For simplicity of illustration the figuresaddress the simpler scenario wherein all data points are symmetrical butin various implementations some or all of the links are unidirectionalor have asymmetric strengths (such as +1.5 in one direction and +0.5 or−0.5 in the other direction).

The data network may be refined based on an active feedback loop fromconcerning the effectiveness of the recommendations provided by thesystem 100. Links can be refined (in either direction) based on feedbackfor how effective the recommendation was. One measure of theeffectiveness of the recommendation is whether funds were spent by theuser based on the recommendation, which in turn might be measured viadata provided by partners such as financial transaction card issuers.Another measure may be feedback provided by the user in response to aquery or survey concerning the recommendation or venue in question. Yetanother measure of recommendation effectiveness is a user's browsingbehavior and the fact that the user left a positive review for therecommended venue on a third party site [which review is collected andparsed as set forth above). Still another technique to assesseffectiveness of a recommendation is geographic dwell time at a physicallocation associated with a venue as measured by mobile device GPS data,for instance.

It should be noted that not only first order connections are updatedbased on feedback. Rather, in various implementations second and higherorder connections are optionally updated based on feedback. Forinstance, when a reviewer's ranking or grade is updated the second orderconnection between two restaurants which are both liked by the revieweris updated or correspondingly modified as well.

Mismatch between the recommendation and the user's evaluation can drivea reduction or weakening of the links between the associated nodes andthe converse could also be executed. In response to positive feedbackbetween a reviewer node's recommendation the links between that node andneighboring nodes may be strengthened. Similarly, links created by thereviewer's reviews may be assigned a greater strength.

The nodal structure facilitates computations and scaling of the network.As will be seen, the nodal network creates a natural look-up table thatis convenient to search and operate over. The nodal structure withinter-node links of varying types provides a convenient way to updatethe structure as new pieces of information are added, and in certainembodiments this is executed without losing the original information asin traditional databases that represent affinity as single numberweights between items. The data in various embodiments is represented aseither an indexed rows of databases, linked lists, or distributed files.

The matrix of interrelationships or links can be broadly categorized ascontent-based interrelationships, collaborative interrelationships andcontent-collaborative interrelationships. The first type, content-basedlinks, are in certain embodiments premised on venue attributes formultiple venues reviewed by same reviewer. The content-based linksestablish interrelationships between venues based on shared attributes.The strength of the link (or anti-link) is dependent on the number ofthings held in common, comparative ratings and other factors asdescribed herein.

Collaborative venue interrelationships associate venues that are likedby same reviewer, often without any dependency or relation to thereason(s) why the reviewer likes the venue. The strength of the link (oranti-link) is dependent on reviewer rating, proximity on same list, andother factors described herein. Collaborative links arise when twovenues co-occur, for example, in the same person's list of favorite orpreferred venues, on the same “top 10” or other grouping lists onranking or recommendation sites, or on the same search engine searchresults. Proximity within the list may be used as a variable to controllink strength. Ant trails may also be used to create collaborative linksby tracking people's surfing behavior and linking venues a given useroften visits, independent of spiderwebbing. In this way, restaurant Amay be deemed interrelated to museum B if many tracked users visit bothof those sites, The user's dwell time at each site or the fact that auser left a rating or review may also factor into whether a link iscreated. In certain embodiments, this tracking is accomplished withoutthe use of cookies, rather by collecting from the web data concerningthe user's activities on rating and review sites according to thetechniques described elsewhere herein.

Content-collaborative interrelationships or links arise from common (oranti-common) reviewer attributes for reviewers who liked (or disliked)the same venue. The venue attributes may be analyzed for common oranti-common features and links may be established between either aspecific venue and reviewer attributes or between venue attributes andreviewer attributes. The strength of link may depend on the incidence ofan attribute among reviewers giving venue a certain grade or similarcomparative ratings.

The exemplary architecture illustrated in FIGS. 3-12 facilitates incertain embodiments dynamic updating and adapting of the network. Forexample, when a new restaurant or review is added to the network, thosenodes each create first, second and higher order links which are addedto the network. The affected links can be updated by a relativelycomputationally simple (and non-resource intensive) addition or otherarithmetic operation and the neural network need not be substantiallyentirely recalculated or reformed.

Generating Recommendations

Either the system or users may trigger the recommendation engine. Theusers may do so by entering through a web portal, client application orelectronic message a request that a recommendation be generated based onprovided venue attributes such as type, geography or price. The system100 may access a user profile to collect data from the user profile suchas other venues liked, gender, profession, or age. The system 100 mayalso automatically generate recommendations for inclusion in electronicmessages, such as text messages or email messages, sent to targetedusers or for presentation on a web portal or client application accessedby users.

The recommendation engine responsively identifies the venues withstrongest links according to the following protocols in selectedembodiments. Based on the identified “liked venue(s)” the system 100identifies the top N venues that have strongest link value to that theidentified venue and which have the specified venue attributes.Alternatively or in addition, based on highest rated venue(s) havingspecified attributes the system 100 identifies the top N venues thathave strongest link Value to that the identified venue. Still anotheralternative which can be used alone or in combination with the foregoingis to, based on the highest rates venue(s) having specified attributesand being recommended by friends or selected reviewers, identify the topN venues that have strongest link value to that the identified venue.The recommendation engine may also generate recommendations based on theuser's attributes, for instance by identifying the top N venues thathave strongest link to user attributes. Further, in selected embodimentsa recommendation threshold at which the nodal link strengths have tocross may be implemented such that the recommendation engine will notrecommend venues with link strengths below the recommendation threshold

In certain embodiments, a plurality of these techniques are used andresulting venue recommendations are weighted based on empiricalobservations concerning the predictiveness or accuracy of each protocol.The weight factors may be simple coefficients or first, second or higherorder equations.

In the case of recommendations provided for a group of users, these sametechniques may be used but with the modification that the userattributes are selected to match the group, either by direct user inputor by arithmetic blending or averaging the user attribute values toarrive at a composite group user profile.

Recommendations may also be provided based on real-time locationinformation, such as that provided by smart-phone GPS data. As describedmore fully below, the system 100 may send an electronic message or alerteither including a recommendation based in part on the location and/ortime or prompting the user to access an interface to receive therecommendation. For instance, if a user is known to be proximate to atheater shortly before a show which the recommendation engine rankshighly for that particular user the system 100 may generate anelectronic alert to the user including the recommendation, a hyperlinkto the system 100 web portal, or a link to active a clientrecommendation application which can launch the interface describedherein.

Alerts or recommendations may be accompanied by, and be generated basedon, promotional offers related to the venues. For instance, anelectronic notification may contain a recommendation along with apromotional discount offer for the related potential booking orreservation. Recommendations presented in the interface (or viaelectronic messages) may also be selected based in part on promotionalstatus. That is to say, the recommendation engine may strengthen linksnodes associated with promotional offers and thus the engine will factorin promotional offers when determining nodes to recommend (i.e. thosemost strongly linked to nodes associated with the user or arecommendation request).

Users' feedback concerning recommended venues and the associated “takerates” may likewise be factored in by the recommendation engine. Forexample, the link strengths may be increased for venues for which usersmore frequently make reservations based on the recommendations,consistent with the techniques taught herein.

EXAMPLE

FIGS. 3-12 illustrate one simplified implementation of therecommendation engine described herein. Those skilled in the art willunderstand that this example can be extended to incorporate any or allof the additional features described herein. Selected of thesesubstitutions and extensions will be mentioned below and thoseexplanations are not intended to be limiting.

FIG. 3 shows an exemplary matrix of reviewer ratings. Reviewer 1 hasprovided reviews for nine out of the twelve restaurants, the ratingsspanning from one star to five, five being the highest. Reviewers 2-7have likewise each provided ratings for a different subset of the twelverestaurants. In other embodiments the venues could be venues ofdifferent types, such as four restaurants, four night clubs and fourtheaters. The ratings may use a wider numerical or alphabetic scale,integer or non-integer.

FIG. 4 shows the corresponding matrix of attributes for the venues ofFIG. 3. In this example each restaurant is in Boston, Mass. and theprice varies on a ten point scale. Attire is assigned alphabetic codes(formal and casual), although numeric codes are used in certainembodiments. Zip codes are used as neighborhood values in this example.The hours of operations is assigned a code selected from a predeterminedlibrary of operational hours and in other embodiments the hours ofoperation is provided various fields, one for each day of the week.

FIG. 5 shows the reviewer attributes for Reviewers 1-7, as shown in FIG.3. In this example, reviewer attributes are limited to gender, age,profession, education, marital status, number of children, number ofreviews, and review accuracy. The codes may be selected frompredetermined libraries. The number of reviews is based on the datacollected as described above. The review accuracy may be calculatedbased on the feedback control data as discussed above. Alternatively, acomposite reviewer grade may be used which optionally factors in numberof reviews, citations of reviews on other sites, number sites hostingreviews and/or consistency of recommendation with positive userfeedback.

FIG. 6 is a chart showing an array of user attributes for seven users.The methodology is similar to that set forth above for reviewers butadditional or different data fields are used for the users. In thisembodiment, each user is asked for four favorite venues. In otherembodiments, a list of preferred venues in various different venuecategories is included in the user profile. This user data, as notedabove, may be input by each user and/or collected from web data sourcesin the manner set forth above.

FIG. 7 is an array of content-based venue links based on the venueattributes of FIG. 4. Restaurant 4 has one link with Restaurant 2associated with common attire. The value of the link, +0.25, is lessthan the other links such that it has a lesser impact on therecommendation, as will be seen. In other words, the link is relativelyweak. Restaurant 4 has three links with Restaurant 1, +1.25 associatedwith the common neighborhood, +1 based on the common genre and +0.25based on the same attire. The net value of the content-based linksbetween links Restaurant 4 and Restaurant 1 is +2.50. This matrix couldoptionally include links associated with a plurality of additional venueattributes and could also include anti-links, or negative links,associated with anti-common properties as will be illustrated inconnection with FIG. 8.

FIG. 8 is a matrix of collaborative venue links based on the reviews setforth in FIG. 3. Taking as an example the association between Restaurant7 and Restaurant 3, there is a +1 link associated with the fact thatReviewer 2 rated both of these restaurants as four star. Restaurants 6and 7 are given a stronger positive link based on common positivereviews because Reviewer 3 rated both restaurants as five star.Returning to the link between Restaurant 7 and 3, an anti-link of −0.75is assigned based on the opposite affinity for these restaurantsexpressed by Reviewer 1 (who gave the Restaurant 3 four stars andRestaurant 7 one star). A higher negative magnitude could be used wherea review rated restaurants in a more strongly opposite manner (i.e. onestar and five star) as shown in the link between Restaurant 11 andRestaurant 5. There a −1.00 anti-link is shown based on the onestar/five star ratings of Reviewer 5. As noted above, a greater array ofdifferent links could be assigned based on commonalities oranti-commonalities—these are merely representative.

A matrix of content-collaborative interrelationships (not shown) mayreflect links arising from common or anti-common features between eachvenue and each reviewer. For example, reviewers may have acharacteristic called “genre affinity” and when that matches the venuegenre a link of predetermined strength may be created. Additionally, thecontent-collaborative matrix may show links between affinity for a venueand reviewer attributes. In that example, common attributes amongreviewers who rated a venue highly are linked to the venue. Forinstance, reviewers aged 31-35 may disproportionately rate a venuepoorly, in which case an anti-link is created between the venue and thereviewer attribute “age 31-35.”

FIG. 9 shows illustrative outputs of the recommendation engine based ona query for a recommendation for an American restaurant and a useraffinity for Restaurant 7 (taken from the subject user's profile of FIG.6). In other embodiments more inputs may be used, such as venueattributes and other preferred venues. In this example therecommendation is a blending of the content-based link strength 901,collaborative link strength 903, and content-collaborative link strength905. Each link strength is assigned a distinct weighting factor 902,904, 906, although in other embodiments the blending equation is asecond order or higher order equation rather than a first order sum ofproducts. The values 910-914 derives from the fact that Restaurant 3 andRestaurant 7 have no link shown in FIG. 7. The same is true forRestaurant 6/7, while Restaurants 9/7 and 12/7 show a +0.25 link.Similarly, the matrix in FIG. 9 shows the cumulative link strengths915-918 for restaurant links 3/7, 6/7, 9/7 and 12/7, respectively. Thecontent-collaborative link strength are based on thecontent-collaborative link matrix (not shown). The weighting factors902, 904, 906 are constant but may be set to vary according to thepredictiveness or accuracy of each type of link (based on feedbackcontrol as discussed above). The resulting recommendation values 920-923reflect the overall link strength 907 between each restaurant andrestaurant 7 as shown above.

Second order relationships could also be included in the link matricesused to calculate overall link strength. For example, Restaurant 8 isliked by both Reviewer 4 and Reviewer 5. Those reviewers, in turn, bothlike Restaurant 5. Restaurant 5 could be assigned a direct +0.25 link toRestaurant 8 based on this second order relationship. That link couldoperate in the matrix independently of the nodes associated withReviewer 4 or Reviewer 5.

An alternative form of second order relationship is shown in FIG. 10.FIG. 10 illustrates second order links arising, from collaborative venuelinks. As shown in FIG. 8, Restaurant 8 is positively linked to bothRestaurant 3 and Restaurant 5, so a +0.25 link is created directlybetween Restaurants 3 and 5. Restaurants 12 and 7 are both negativelylinked to Restaurant 8 so a +0.15 link is created to reflect the beliefthat this anti-link is weaker than the positive link previouslymentioned. In a similar vein, an even weaker second order link isestablished between Restaurants 11 and 12 because while both arenegatively linked to Restaurant 8 the links are substantially differentin magnitude.

These second order relationships can be added directly to the relatedmatrices or otherwise computationally combined when calculating overalllink strength between two nodes.

FIG. 11 shows an arbitrary set of link values in a more complex systemthat factors in a wider variety of links (such as second order links)across the same nodes. It can be seen that the values are stronglypositive and few values are negative. This can be observed where thedata has a skew associated with reviewer tendency to give generousratings, for instance. If the data of FIG. 11 is content based it mayhave a skew different than parallel matrices for collaborative links orcontent-collaborative links. Accordingly, it may be useful to normalizethe data of FIG. 11 to facilitate computational combination with linksin the other matrices.

FIG. 12 shows the data after an exemplary correction operation. In thisexample, a constant value of five was subtracted from all data points.In other embodiments, the value subtracted may be selected such that thedata set hits a common or desired mean or median.

In other embodiments normalization is accomplished by multiplication ordivision, For example, a certain percentage may be subtracted like a taxfrom affected links by multiplying the link strengths by (1−X), whereinX is a tax rate from 0 to 1. The tax rates in this approach may beprogressive to accommodate the tendency of users and reviewers toaggregate toward a small number of more popular venues, which asdiscussed herein can cause those venues to cast too large a shadow orhave an undue influence on the remainder of the neural network.

It should be noted that normalization can occur at local level or at thenetwork level. At the local level, all links connected to a certainnodes may be normalized or all links coming to or going from a certainnode may be normalized (recalling that links may be unidirectional orasymmetric). Alternatively, normalization may occur at the data matrixlevel. For example, content-based link matrices may be normalized orother data subsets of network may be normalized.

FIG. 13 shows another form of higher order connection, connection creep.In this example the link between Restaurant 10 and Restaurant 1 in FIG.12 is considered too high in that it might have an undue influence onthe connected nodes. Accordingly, 1.5 of link strength is subtractedfrom link 10/1 and 0.5 is added to the less strongly positive links10/2, 10/7 and 10/8. No portion of link 10/1's strength is reassigned tolink 10/9 because it is already above a predetermined threshold abovewhich links are not to have connection creep bonuses added or abovewhich no higher order links should be added.

User Interface

FIG. 14 is an exemplary user interface for deployment at a web portal orclient device such as a desktop computer, smart phone, tablet PC,automotive multimedia interface or other mobile computing device. Theserver or local application provides an evolving personalized brand logoand personalized audio soundtrack to match the displayed itinerary. Thesound track may persist and “travel” with the user as he or shenavigates different functionalities or pages through the interface. Theinterface is also designed to provide bio-visual data feedback to theuser. The system permits users to state their goals and intentions basedon the feedback they have received from the system.

FIG. 14 is an overview page that provides users with an immediateperspective on options, a space forcollection/comparison/pre-screening/deliberation, and the ability toimmediately act. Specifically, the overview page has three distinctsections and functionalities.

First, at the recommendation panel 1410, a plurality of recommendationsare presented. In preferred embodiments, there are five recommendationsprovided as shown in FIG. 14. In other embodiments, two to seven, threeto six, four to six, four to eight, four to nine, or two to tenrecommendations are provided. The number of recommendations may be on aper-venue basis so that five recommendations are provided forrestaurants and a like number of hotels are recommended. Alternatively,a lesser number of complementary venue (e.g. hotel) recommendations areprovided.

Second, the collection and comparison panel 1420 provides a place tocompare and contrast recommendations of interest. The panel providesvenue genre or type, the venue name, geographic area, and price. Thepanel also provides buttons to book a reservation or checkavailabilities or rates for the various venues. Buttons for adding theevent to the Ava calendar (discussed below) are optionally providedadjacent each venue. Also provided are status identifiers indicating thecurrent state of activities and/or bookings for each venue. Optionally,buttons may be provided to launch a window or image that depicts thevenue on a map.

Third, the calendar panel (not shown) will feed or import a view of theuser's personal Ava calendar and provide interactivity for immediateassessment of the user's schedule and available times. The calendarpermits import of the user's other appointments and export of the Avacalendar items to any third party calendar systems such as Outlook,Google, and iCal.

These three panels are arranged down the page so that decision-makingflows down the page from menu of options (top), to deliberation andcomparison (middle), to arriving at a decision, and finally toscheduling/booking/publishing/sharing/taking action (bottom). Thisarrangement may in certain embodiments facilitate decision-making.

A user can directly book a recommendation at any of these three stages,or add to calendar at either of the first two stages. This arrangementmay in certain embodiments enhance the likelihood that a user makesreservation or booking based on the recommendations.

Additional optional functionalities (not shown) include a transportationreservation interface. For example, the interface may present atransportation button that launches an booking or reservation portalwhich communicates with a third party transportation provider, such as ataxi service, and makes a reservation corresponding to a restaurant orother reservation. The interface may also permit the arrangement oftransportation services between and amongst a plurality of otherrecommended events spanning one or more days.

In similar vein, booking functionality may be provided for a variety ofcomplementary venues, services or activities. Examples include hotelrooms, airline reservations, movie tickets, theatre tickets, museumtickets, music tickets, sporting events, product delivery (such asflowers or flowers), real estate services, or moving services (such asinter-city packing and transportation services).

The interface may selectively suggest alternative actions or venuesbased on a first booked venue or action. For instance, the booking of arestaurant reservation may prompt the generation of night club ortheater recommendations. As another example, the booking of a realestate tour through a real estate agency may prompt a recommendation formoving services. Subsequent bookings may in turn generate additionalrecommendations complementary to the most recent booking, the earlierbooking, or both.

These follow-on recommendations may be filtered and selected accordingto the techniques set forth above. In particular, the recommendationsmay be function of the user's profile, attributes, venue preferences,past booking behavior and/or previous feedback concerning certainvenues. For instance, the recommendations may be filtered as set forthabove according to the user's most recent reservations and the user'sexpressed preferences for given venues that are linked to potentialsecondary or tertiary recommendations.

Recommendations may also be provided based on real-time locationinformation, such as that provided by smart-phone GPS data. The system100 may send an electronic message or alert either including arecommendation based in part on the location and/or time or promptingthe user to access an interface to receive the recommendation. Forinstance, if a user is known to be proximate to a theater shortly beforea show which the recommendation engine ranks highly for that particularuser the system 100 may generate an electronic alert to the userincluding the recommendation, a hyperlink to the system web portal, or alink to active a client recommendation application which can launch theinterface described herein.

Alerts or recommendations may be accompanied by, and be generated basedon, promotional offers related to the venues, For instance, anelectronic notification may contain a recommendation along with apromotional discount offer for the related potential booking orreservation. Recommendations presented in the interface (or viaelectronic messages) may also be selected based in part on promotionalstatus. That is to say, the recommendation engine may strengthen linksnodes associated with promotional offers and thus the engine will factorin promotional offers when determining nodes to recommend (i.e. thosemost strongly linked to nodes associated with the user or arecommendation request).

Users' feedback concerning recommended venues and the associated “takerates” may likewise be factored in by the recommendation engine. Forexample, the link strengths may be increased for venues for which usersmore frequently make reservations based on the recommendations,consistent with the techniques taught herein.

Users may be provided a profile page or “my account” page that providesanalytics on that data and any other data collected or contributed toprovide perspective and insight into behavior. The page provides afeedback mechanisms to the user that is “habit honing” in that analyticson self activity is provided in a visual format. For example, the pagemay present graphical trends of actions within customizable goalcategories such as health (gym, yoga), family (museums, travel, dining),and errands (dentist, mechanic, groceries 1. Based on user definedgoals, the overview page suggestions can be featured to highlightrelevant activities to fill existing calendar time-slots.

The interface may also provide other prompts to facilitate action andhone habits. For example, the interface may provide cues and triggersembedded in mobile device applications to cue initiation of plans andtransitions between scheduled events. For instance, the mobile clientapplication may trigger chimes upon next scheduled event, music toreduce anxiety surrounding errands, tailored music transitions upon theoccurrence of the next scheduled event, or visual (blinking LED) cuesupon next scheduled events.

The interfaces described herein may be presented, as noted, through avariety of devices. Still additional devices are contemplated, includingtelevision screens, third party websites (through partnerships),in-store kiosks, or personal keychains or dongles.

Bio-Visual Personalized Feedback

The user interface described above presents a plurality ofrecommendations to the user based on a user search query therebyallowing a user to pick various venues and/or add them to a calendar.However, based on this view, a user may not fully grasp theinterrelationships between the venues that led the recommendation engine112 to recommend the venues in the first place. Further, as long as therecommendation engine 112 is providing recommendations to the user, theuser may not have as much of an incentive to provide more information tothe system 100 thereby enabling an enhanced neural network topology thatwill provide better recommendation results.

To ensure a better user understanding of the nodal links between venueswithin the neural network topology and to provide motivation for usersto input more information into the system 100, an additional bio-visualpersonalized feedback interface is provided to users of the system 100.Through the bio-visual feedback user interface, the user is able to viewan overall network topology of various nodes within the system 100 andthe interconnections therebetween thereby allowing users to grasp thenodal link strengths and relationships between venues.

The bio-visual feedback interface is a two-dimensional interface thatprovides an overhead aerial “bird's eye” view of the network topologypresented topographically in an easy to understand manner. For instance,at the highest level, a topographical view of the system may depictclusters of nodes in various regions with some node clusters beingvenues of interest to the user based on previous recommendations and/ornodal interrelationships having strong overall link values and othercluster areas may include nodes known to have low overall link scoresor, in other words, nodes that would be bad recommendations to the user.To enhance the understand of the user, nodal clusters having nodes thatwould be strong recommendations for a user or nodes that have previouslydetermined to have been effective to the user can be displayed in hotcolors, such as red or orange, whereas other nodal clusters having poorrecommendation choices can be displayed in cold colors, such as blue orpurple. Nodal links may or may not be provided at this level to give theuser an understanding of how various nodes are linked to each other.

The topographical view of the nodes can further be enhanced via contourmapping features. Accordingly, certain areas of the clusters whichinclude strong nodal links therein can be presented in an “elevated”contour with a different color mapping than venues with lesser overalllink strengths positioned in lower elevations. Further, nodes havingantilinks with other nodes may be positioned farther apart in the aerialview and have dotted links to indicate the negative link strengththerebetween. Further, nodes that do not have links to other nodes maybe placed apart from the nodal clusters to represent their independencefrom the other nodes of the neural network.

FIG. 15 illustrates an exemplary bio-visual personalized feedback userinterface using a topographical contour mapping system based on a usersearch query for Restaurant 7. As illustrated in FIG. 15, the neuralnetwork topology, or neural network “world” view 1500, includes nodesrepresenting Restaurants 1-3, 7-11 and Restaurants X and Y. Restaurants1-3 and 7-11 are the same Restaurants illustrated in FIG. 4 andtherefore have the same venue attributes, content-based relationships,collaborative relationships and content-collaborative relationships asdescribed above with respect to at least FIGS. 5-9. Accordingly, thelinks shown between the nodes are in accordance with the content-basedvenue links and collaborative venue links identified in FIGS. 7 and 8.Restaurants X and Y represent two venues that are not linked to anyother venues within the neural network. In selected embodiments, theworld view 1500 represents the “highest” aerial view the user can obtainvia the bio-visual feedback user interface.

As described above, Restaurants 1-3 and 10 represent a cluster of venueshaving strong overall link strengths with Restaurant 7 and as such maybe represented using hot colors. Restaurant 7 itself may be indicated ina neutral color to identify that it is the restaurant expressed in thesearch query by the user. Further, Restaurant 7 could be displayedlarger than the other nodes in the topology depicted in FIG. 15 toidentify it as a restaurant that was part of a search query. Restaurants8, 9 and 11 may be depicted in cold colors as they have lowrecommendation values based on low overall link strengths with respectto Restaurant 7. Further, as illustrated in FIG. 15, the links betweenRestaurant 7 and Restaurants 8, 9 and 11 are dashed as they representanti-links having negative overall link strengths.

FIG. 15 further includes elevated contour zones 1501, 1502, 1504 and1506. These topographical contours represent elevated nodal areascontaining nodes having strong overall link strengths with a node ornodes included in a search query. Accordingly, contour zone 1501includes Restaurant 7 and Restaurant 1 and is depicted as having thehighest elevation as Restaurant 1 has strong content-based andcollaborative venue link strength. Further, contour zone 1502 has amedian elevation and includes Restaurant 10 which has a higher overalllink strength with Restaurant 7 than Restaurants 2 and 3 but has a loweroverall link strength than Restaurant 1. As Restaurants 2 and 3 have thelowest overall link strengths with Restaurant 7 when compared toRestaurant 1 and Restaurant 10, they are depicted at the lowestelevation or “ground” level. Therefore, contour zones 1501 and 1505provide the user with an easy-to-understand view of restaurants thatwould be good recommendations with respect to Restaurant 7. On the hand,contour zone 1504 and 1506 represent elevations containing nodes thatwould be bad recommendations as contour zone 1504 and 1506 contain nodeshaving lower overall link strengths with respect to Restaurant 7. Forinstance, contour zone 1506 includes Restaurant 9 and Restaurant 11which have comparable negative overall link strength values with respectto Restaurant 7 but that are elevated higher than Restaurant 8 asRestaurant 8 has a lower negative overall link strength value withrespect to Restaurant 7. Accordingly, alternatively from the depictionof Restaurants 2 and 3, the lowest elevation or ground level may bedepicted via a contour zone.

The system 100, as illustrated in FIG. 15, can also display links tonodes in other locales. For example, in selected embodiments, the worldview 1500 may include different locales such as New York and Boston. Assuch, link 1508 identifies a connection between Restaurant 7 of Bostonand Restaurant Z of New York. The link between different locales may berepresented in various manners such as a squiggly line in order toindicate to the user that the link represents a connection to adifferent area. Further, as the line is solid and not dashed itrepresents a positive overall link strength between Restaurant 7 andRestaurant Z as described above. While not shown, New York will likelyhave its own neural network topology with corresponding contours andinternodal connections. Accordingly, the bio-visual personalizedfeedback user interface provides the user with the ability to quicklyidentify restaurants in different locales that may be strongly connectedto known restaurants in the users location. In other words, if the useris planning on traveling to New York he can “virtually travel” overtransit link 1508 to determine restaurants in New York that he may wantto visit based on their relationship with Restaurant 7.

To further aid the understanding of the user upon a close or cursoryinspection of the bio-visual feedback user interface, the contour zonesmay also be colored using hot or cold colors based on overall linkstrength with respect to a designated restaurant. The more informationthe system 100 contains, the more detailed and comprehensive contour mapcan be provided to the user. Accordingly, by interacting with thebio-visual feedback user interface, the user is motivated to providemore information to the system in order to discover new avenues ofconnectability with respect to the nodes of the neural network topology.This in turn enables the system 100 to provide more accurate informationto the user for future recommendation requests.

In addition to viewing network topology via the bio-visual feedback userinterface, the user may interact with the nodes in order to redefine andredisplay the neural network image. For instance, world view 1500identifies the neural network topology with respect to Restaurant 7 butthe user may designate, via a mouse, speech or other input device,another node in which to view the network topology. The system 100 willthen recalculate the overall link strengths as previously describedherein and will redisplay the world view 1500 based on the newlydesignated node. Therefore, the bio-visual feedback user interfaceallows the user to easily grasp interconnections between a variety ofdifferent nodes and to realize an expansion of internodal links based oninput provided by the user.

While FIG. 15 illustrates a world view 1500 representation, the system100 also allows the user to zoom into lower levels of “aerial coverage”.Accordingly, if there are too many connections between nodes in variousclusters, the user may not be able to fully grasp specific connectionsbetween specific nodes. Therefore, the user can zoom in on a particularportion of the world view 1500 to see more granular arrangements ofnodes and connections therebetween. The user may zoom in by clicking amagnification button (not shown), by right clicking a specific nodewhich pops up a dropdown menu having an option to zoom in with respectto that node, by speech, touch or any other method as would beunderstood by one of ordinary skill in the art.

FIG. 16 represents a local view 1600 of the world view 1500 of FIG. 15based upon a command from the user to zoom in with respect to Restaurant1. In other words, the local view 1500 illustrated in FIG. 15 depictsnodal connections based on Restaurant 1. In selected embodiments, thesystem, whether in the world view 1500 or local view 1600, may center onthe screen whichever node has been selected by the user. FIG. 16 isintended to be exemplary and therefore only a handful of the connectionsare illustrated for the ease of explanation. Accordingly, Restaurant 1has a variety of connections with Restaurants 2, 3, 7, 9 and 10 and theuser is able to grasp a better understanding of these connections ascompared with the world view 2500. For instance, different linkstrengths are represented by different link thicknesses and anti-linksare represented by dashed lines. The user can further designateparticular lines themselves and the system 100 will display the overalllink strength values for that particular connection.

As illustrated in FIG. 16, link 1602 connecting Restaurants 1 and 7 hasthe thickest line as compared to other connections as they share thesame neighborhood and genre and both received highly positive reviewsfrom Reviewer 2. Link 1606 connecting Restaurant 1 and Restaurant 2 hasa small line thickness as they only share the same attire and do notshare any positive review data. Link 1604 connecting Restaurant 1 andRestaurant 9 is dashed as they share a negative overall link strengthdue to an opposite affinity expressed by Reviewer 2. However, the link1604 is not that thick as the overall link strength value, whilenegative, is close to zero.

In the local view 1600, the links may be depicted in different colors toidentify which links have strong or small overall link strengths withrespect to a designated node. For instance, link 1602 may appear to bebright red due to a strong overall link strength between Restaurant 7and Restaurant 1 and link 1604 may appear blue based on a low overalllink strength value between Restaurant 1 and Restaurant 9.Alternatively, the user may request link colors solely based on thedesignated node such that link 1606 would be assigned a “hotter” colorthan link 1602 because it is a direct link with Restaurant 1. Further,links having a stronger overall link strength with respect to adesignated node may appear closer to the designated node than otherlinks. For instance, Restaurant 7 is closer in proximity to Restaurant 1than Restaurant 3 as Restaurant 3 has a lower overall link strength withRestaurant 1 as compared to the overall link strength value betweenRestaurant 7 and Restaurant 1. In addition to or alternatively, otherindications may be used to designate link strengths such as making nodalappearances larger than others if they have a strong overall linkstrength with respect to a designated node.

Therefore, both the world view 2500 depicted in FIG. 25 and the localview depicted in FIG. 16 provide an easy-to-understand view of theneural network topology thereby providing the user with insight intopotential recommendations while also providing an incentive to presentmore information to the system to bolster internodal links. While thenodes illustrated in FIG. 15 and FIG. 16 are represented by the numberof the restaurant, other depictions are contemplated such as thumbnailpictures, logos and/or names of the restaurants. Further, the system inselected embodiments may display a three-dimensional view of the neuralnetwork topology thereby giving the user a real-world impression of theinternodal connections. For example, in a three-dimensional system,connections with strong overall link strengths may represent highwayswhereas connections with low overall links strengths may be representedas dirt roads. In addition to or alternatively, venues that are locatedclose to each other which are thereby in “walking distance” mayrepresent strong overall link connections as opposed to venues thatappear far off in the horizon.

Collaborative Decision Making

Group events can often cause problems between individuals in the groupbecause it may be hard for everyone to come to agreement on particulartopics. For instance, a group of friends that are also users of thesystem 100 may have plans to go out to dinner but may be unsure of whichrestaurant to go to or may be unable to come to an agreement withrespect to a restaurant. As such, each user may have his own opinion ofwhere the group should go based on recommendations provided by thesystem 100. Therefore, in addition to providing users with a variety ofways in which to view recommendation data and nodal connections betweenvarious venues, the system 100 also provides user interfaces enabling agroup of users to collaboratively select venues.

Initially, the system 100 performs a group search using informationprovided by members of the group. Specifically, the members of the groupmay submit a venue choice and a list of requirements to the system 100in which they want the search query to adhere to when determiningrecommended venues. As such, some members may specify the genre whereasother members may request a low price point. The recommendation engine112 will then perform a search for each particular member based on theirindividual requests and recommendations are generated by therecommendation engine 112 as previously described herein such that eachmember has their own recommendation set.

Once the recommendation engine 112 has generated the variousrecommendation sets for each member of the group, the collaborativedecision making user interface depicts each group member in a differentrow with recommendations for each corresponding member appearing in acolumn beneath that member. The system 100 also highlights one of therecommended venues for each member that represents the strongest overalllink strength based on the venue provided by the user and the filterstate input as part of the search query. The highlighted venue may bedisplayed in a certain color, have a varied border as compared to otherrecommendations, may be displayed larger than the other recommendationsor may be displayed in a particular order with respect to the positionof the member information. The recommendation engine 112 will alsodetermine, based on the information included the search queries by eachmember, a recommended group venue having the greatest average affinitywith respect to the recommendations identified for each member. Therecommendation engine may also use attribute information to preventclashes between members of the group. For example, the system may notrecommend a steakhouse as a group venue when one or more of the membersof the group are known vegetarians. Further, for example, if a majorityof group members dislike seafood, the recommendation engine may avoidgenerating a seafood restaurant as a group recommendation regardless ofhow close other calculations come with respect to other attributes suchas price and attire. Accordingly, to generate a group recommendation,the recommendation engine 112 takes into account at least the nodalinterrelationships between venues identified by the group members anduser attribute data.

Once the recommendation engine 112 determines the group recommendation,the system 100 highlights the group recommendation if it already appearsin the user interface and/or displays the recommended venue separatelyfor the members of the group to see. In selected embodiments, eachmember of the group will have the ability to vote on the recommendedvenue or to select a different venue from the options listed on thedisplay screen. The recommendation engine 112 will then continuouslyrecalculate a recommended group venue or venues having the strongestoverall affinity based on the recommendations provided by recommendationengine 112 or those voted on by the members. To prevent an endlessrecommendation cycle, each member may be limited to a certain number ofvotes and/or a time limit, such as a certain time before the plannedevent, may be prescribed to ensure a limited voting period.

FIG. 17 illustrates an exemplary collaborative decision making userinterface, or group interface. In FIG. 17, Members 1-4 represent a groupof users of the system 100 who are planning to meet for dinner on Oct.8, 2012, at 5:00 PM. In selected embodiments, one user may setup themeeting time of an event and can send invites to other users of thesystem 100. If these users accept the invite, the system 100 willindicate that they are part of a group. Once the group is formed, theusers may each provide a venue and other filter parameters to have therecommendation engine 112 provide a recommendation set for each user. Inother embodiments, the recommendation set of a founder of the group mayhold more weight or may be the only recommendation set provided to thegroup members. Once the recommendations have been determined by therecommendation engine 112 for each user, the system 100 displays theserecommendations as illustrated in FIG. 17 such that each user is shownalong with their corresponding recommendations. Within each columncontaining the recommendation set for each user, a recommended venue(1706, 1708, 1710 and 1714) having the strongest overall link strengthwith a venue provided by the user as part of the search query ishighlighted. Some users may have more recommended venues in theirrecommendation set based on the development of the neural network withrespect to the particulars of the search query made by that user. Forexample, Member 4 only was provided three recommendations by therecommendation engine 112. Once the individual recommendations aregenerated, the system 100 then calculates a group recommendation havingthe greatest average affinity based on the recommendations identified bythe recommendation engine 112 and presents the venue as a current grouprecommendation 1700. The previous group recommendation 1702 may also beprovided to illustrate to the members of the group that the venue haschanged.

The collaborative user interface also identifies the last time at whichthe group recommendation was changed as well as the last vote for avenue and who placed that vote. Further, a deadline for submitted votescan be set by the group founder and/or voted upon by group members andis displayed to inform group members of the last time at which a votemay be cast. Accordingly, as the meeting time is set to 5:00 PM on Oct.8, 2012, the group members must submit their final votes, if any, byOct. 8, 2012, at 12:00 PM. As such, the initial group recommendation isdynamic and can change up to the deadline by receiving different votesand recalculating the group recommendation based on overall linkstrengths and user attribute data with respect to the newly voted venueand previously identified venues for particular group members. In otherselected embodiments, the group founder may prevent voting therebylocking the group recommendation calculated by the system 100.

In the exemplary user interface illustrated in FIG. 17, the system 100calculated Restaurant 7 as being the best group recommendation 1700based on the individual recommendations generated for each user by therecommendation engine 112. Recommendation 1704 is highlighted toindicate that Member 4 changed his choice for a venue from Restaurant 1to Restaurant 11 by voting for Restaurant 11. This change may have beenmade from the original recommendation made by the system 100 or a from aprevious vote cast by Member 4. For the purposes of this example, it isassumed that the recommendation engine 112 originally generatedRecommendation 1704 and based on this recommendation in conjunction withthe Recommendations 1706, 1708 and 1710 for Members 1-3, the system 100originally generated the group recommendation 1702 of Restaurant 4.However, as explained further below, the recommendation engine 112recalculated a group recommendation of Restaurant 7 based upon the votefor recommendation 1714 by Member 4. In other selected embodiments,recommended venues other than those highlighted within the userinterface are used by the system 100 to calculate a group recommendation1700.

Group recommendations are based, in part, on recommended venueattributes such as those illustrated in FIG. 4. For instance,Recommendations 1706 and 1708 have the same genre but have differentprice points than Recommendation 1710 and Recommendation 1704 withRestaurant 7 being expensive and Restaurants 1 and 12 being inexpensive.However, as Recommendations 1710 and 1704 have different genres, theJapanese genre of Recommendations 1706 and 1708 is the dominant genre.Accordingly, as the predominant genre was Japanese but the price pointswere opposite, the system 100 originally generated the grouprecommendation 1702 of Restaurant 4 having the same Japanese genre but amedium price point. However, when Member 4 decided to vote forRecommendation 1714, the system 100 recalculated the grouprecommendation and recommended the current group recommendation 1700 ofRestaurant 7. For instance, Recommendation 1714 has a high price pointand therefore only one recommendation, Recommendation 12, has a lowprice point. Further, as Recommendation 1710 and Recommendation 1714have different genres, the Japanese genre of Recommendations 1706 and1708 is still predominate. Accordingly, the system calculated Restaurant7 as the group recommendation as it has the predominate genre and a highprice point. Of course, these examples are for the sake of illustrationand user attributes, other venue attributes, and link strengths based onthe neural network topology between the various recommendations can allbe used by the system 100 to calculate a group recommendation 1700.

The collaborative user interface illustrated in FIG. 17 is exemplary andas such can be displayed in a variety of ways. Members of a group may belisted in columns with corresponding recommendations being listed inrows adjacent to the group members. Members may customize imagesrepresenting their virtual user identity within the system 100.Thumbnail images, logos, or videos may be used in addition to oralternatively to the textual display of the restaurant in therecommendation slots. The strongest recommendations, such as 1706, 1708,1710 and 1714 illustrated in FIG. 17, may be highlighted, may appearlarger than other recommendations, and/or may contain animations oraudio representations of the recommended venue itself. Further, videostreams of group members themselves may be depicted in the collaborativeuser interface via an imaging device to virtually interact with eachother when determining meeting times, discussing group recommendations,or taking votes. Further, a group member may vote on recommendationslisted for the group member or any other group member as well as forvenues not illustrated in the collaborative user interface.

The collaborative user interface illustrated in FIG. 17 thereby presentsusers with the option of having the system 100 generate a grouprecommendation when members are having a hard time determining a venueamongst themselves. The group recommendations further provide the userwith the comfort of knowing that the group recommendation is not onlybased on strong links to the interests of the user but also to othermembers of the group thereby increasing the likelihood of a speedyresolution when determining venues amongst large groups. Further, inselected embodiments, the system 100 may incorporate votes may by usersinto the data repository 118 such that the system 100 may further updatethe neural network topology and enhance future recommendations.Additionally, the system 100 may in real time repopulate therecommendations within the collaborative user interface based on updatednodal links between venues with respect to votes cast by one member orother group members.

Illustrative Implementation

One illustrative system implementation consistent with the foregoingteachings is discussed below. The discussion is generally organized intofour sections: content collection, content organization, personalizationand user interface.

The purpose of the Content Collection system is to perform 3 steps: 1)identify “objects” (venues, events, and other instances of interest tothe user), 2) find/match electronic pages with deep information on thoseobjects (object characteristics, reviews, associations with otherobjects), and 3) retrieve pages into the storage system.

The objects to be retrieval in this example constitute any set of webpages based on objects of interest. The objects may be selected based oncategory, filters for a particular category and the content sources thatare targeted.

This type of retrieval can in turn be broken up into several ContentModes. Content Mode 1 is called “Global Grab.” In this mode, the systemseeks to identify and retrieve information on every object in a category(e.g., “all restaurants in San Diego”). In Content Mode 2, KeepingCurrent, the system seeks to focus the collection on either i)refreshing stale information on old objects, or ii) Identifying newobjects that just arose for old categories. In Content Mode 3, known asIntelligent Browsing, the system seeks to have the data search updateitself dynamically based on its real-time discoveries, to “zoom in” andfocus on specific trends and objects.

One type of Global Grab is spidering. This is a conventional method usedby Internet search engines according to which the system downloads: thepage of a content provider's site, scans that page for links to otherpages on the site, and then downloads those pages. By repeating thisprocess an entire site can be covered. The system can also implementpaginated searches in which the system actively seeks, for example, page1 of a term like “Restaurants,” then page 2, and so on.

A second type of Global Grab is crawling. Sometimes it is desirable notto have to get pages directly from a content site, such as where thesite blocks automated indexing. In this case one can replicate thestructure of a site from the cache of a search engine, which crawl andcache every page as a “second copy” of the internet. Here, the systemuses a search engine to search for the URL of interest. Usually, the URLwill be included in the first result, along with a “Cached Page” link tothe cached copy of the page. The system can then download the linklisted in the “Cached Page,” which is the same as the original page. Thesystem can then scan that page for links to other pages on the site, andrepeat the process for those pages.

A third type of Global Grab involves getting a list of all objects andthen finding them within a site. This is a method designed to be moreholistic than spidering, to ensure that every single object of acategory is retrieved from a given site if available. First, a completelist of target objects is created, such as by crawling an Internetdirectory like Yellowpages.com for “restaurants in San Diego.” Then thesystem will have the complete list of objects for which data is desired.The next step is to search for each of these objects in turn in a searchengine, restricting the search to the pages from the target website.Different combinations of data extracted from the internet directory canbe used to seed the search query, and usually the business name, metroname, and phone number are useful ways to lock onto the object on thetarget site.

The search engine will retrieve pages that match these search queryparameters on the target site of interest. Usually one of the first fewpages in the results is the correct match. By repeating this searchengine and retrieval process for every object in the Internet directory,the system is likely build, a complete replica of the target site's dataon that category.

A fourth type of Global Grab involves third-party crawlers. It iscontemplated that third party services will crawl the web and make theresults of those crawls available for purchase. In this case, the firststep of the global grab methodology is simplified because the system canquery the service for every page arising from a certain set of websites.If such third party services also make the pages available for retrievalthen the speed of the crawl is increased.

Turning to Content Mode 2, Keeping Current, it is assumed that thesystem has completed a global grab and has data on all objects for agiven category. The task then becomes staying current, or up to date,with the objects as their data changes. New objects can be introduced,such as when restaurants open. Old objects can become outdated, such aswhen restaurants close. Data on objects can change, such as if the hoursof operation or menu items change. New and old objects can be identifiedby doing a crawl on global directories (which is fast) and then focusingin on any changes to the list of objects. Alternatively, the system candiscard old data and then run a new global grab. Finally, the system canrely on “update notifications” which can be acquired in several forms:I) some websites focus on these changes, such as “listings of newrestaurants” in local papers, ii) many content provider APIs will notifyof openings and closings of sites, iii) URLs and webpage titles willoften receive a “CLOSED” stamp which can be rapidly screened. Each datumcollected by the system is tagged with an expiration date, based on thetype of the data (events expire immediately, restaurants may need to berefreshed every few months to check for major changes). Data that hasexpired can have associated pages re-retrieved for freshness. There-retrieval process is simplified because the URL is already known.

Content Mode 3, Intelligent Coordinated Retrieval, involves “eatingnodes,” or retrieval computers, that can coordinate their searches basedon real-time events to optimize content gathering in response to massuser behavior. In this implementation the retrieval computers are given“write” access to the retrieval queue. If the retrieval computersidentify a trend that is similar to their original target, but stronger,the retrieval computers can recruit other computers to look more deeplyat this phenomenon by writing the new target (or a set of targets withina target area) onto the retrieval queue. Retrieval computers can alsointeract intelligently in the collection process by alerting each othersif a lead turns out to be faulty, and is indicative of more faulty leads(for example, if a region of a site is covered with spam or stale data).In this case, the retrieval computer(s) can scan the queue and deletesimilar jots on the queue so that future computers don't devoteresources to exploration of a lower value target area. In this way,different search nodes again inform one another about what they learn byvirtue of the shared queue to help guide their collective search,

Turning next to matching objects to content pages, whenever the systemis gathering data from target websites on an object of interest, thesystem should ensure that the data on the target site is actuallyreferring to the object of interest. This is especially true whenattempting to cross-reference objects across different sites. The systemoptionally utilizes a “likelihood of match” score to make thisdetermination, taking into account multiple variables. For example, ifthe system is trying to match a venue on two different sites, the factthat they have the same phone number or address may tend to indicatethat they are the same venue. Numeric identifiers on consistent scalesare particularly valuable for this purpose, such as phone numbers, UPCsymbols, and latitude/longitude. Non-numeric identifiers (strings) suchas addresses can also be used, and one can check the similarity of thetwo sites' addresses by taking a Hamming distance on the characters, orparsing, out each one's street number, street name, etc.

Data is cross-referenced across multiple sites by using data from onesite to choose objects to find on another site, then use the stepsdiscussed above to find new content pages from those objects on adifferent site.

A fleet of retrieval computers may be created by building each fromscratch programmatically. Each computer is resurrected from a diskimage, such as an Amazon Machine Image (AMI). The AMI is loaded as anelastic computing node on Amazon's EC2 (elastic cloud computing) orother service using standard libraries written in Java. The AMI is armedwith everything that the computer will need, including a Java runtimeenvironment, the capacity to communicate with a central version controlrepository such as Git, etc, The AMI is also armed with a startup scriptthat runs when the EC2 node is born, and receives user parameters passedto the EC2 node at birth. The user parameters to the startup script tellit where to download the latest code instructions for the node, such asthe URL of an S3 location, or the URL of a Git repository. The startupscript is armed with the credentials to access the latest codeinstructions, and load the code onto the new EC2 node. Every EC2 node inthe fleet downloads similar instructions, so they are all prepped arounda common task. These instructions tell it how to connect to the messagequeue with the URLs to retrieve, and also how to go about the retrievalprocess. Each one then launches the downloaded code (runs the JAR file,etc) and thus begins working. Finally, each computer in the fleet isassigned its own IP address (via Amazon's Elastic IP system, etc) sothat they can be throttled by content sites independently from the othernodes and work in parallel.

Tasks are distributed amongst the fleet of retrieval computers by usinga list of URLs (usually long, millions) of pages that the system wantsto retrieve. This list might be a text file, database table, or othersimple serial storage system. The goal is to distribute those URLs amongthe many computers. This process is best implemented through a queueservice that lives independently from all the retrieval computers. As anexample, Amazon offers the Simple Queuing Service (SQS) in which everyURL is stored as a string message on the queue. Thus, the queue retainsa memory of which URLs still are to be crawled. Each computer in thefleet can query the queue for the next item to be crawled. The queuethen assigns the item to a particular retrieval computer, and marks theitem as “locked” so that other retrieval computers do not also try towork on the item. Meanwhile, the system monitors whether the retrievalcomputer completes the task in a timely manner. If the retrievalcomputer does not check back with the queue to say that the job is done,then the queue restores the item to “unlocked” so that other computerscan perform the task. Once a computer checks back with the queue andinforms it that the task has been completed the queue removes the itemfrom the queue. Thus, a workflow is established that can be sharedbetween an arbitrary number of retrieval computers where they canoperate simultaneously to work through a list of retrieval tasks.

Pages are retrieved by all computers in the fleet. Each retrievalcomputer is already armed with a URL to retrieve by taking the messagefrom the messaging queue. The computer then executes a function tostream the contents of the remote file (webpage, etc) into memory (inPHP, fileget_contents; in Java, urLopenStream( ); etc). The computerthen saves this file to the global storage system (see below). Withrespect to rate of repetition, it should be noted that no singlecomputer hits a given content source too rapidly. Therefore, eachcomputer is “throttled” to only complete one page request every 0.1-10seconds. The use of third party crawlers, discussed above, may obviatethe need to throttle in this manner. Every page request is checked todetermine if it succeeded, and if failure occurs, a longer interval isused before the next attempt. The system can implement differentschedules for the interval rollback, such as an exponential rollback.

The global storage system may be a distributed storage platform (Amazon53, etc). In the case of Amazon S3, data is stored in buckets that areaccessible from any computer as a URL. Each retrieval computer storesthe contents of the retrieved file in a repository folder on S3 (orother service) as a file path string which is also URL. The file canthus be retrieved at a later date by entering the storage system URL.Access to these repository folders are private so that they can only beaccessed by the system's Content Collection and Content Organizationsystems.

Turning now to content organization, the aim is to take contentcollected from the Internet and organize it for access through theInterface. The input may be a hard drive directory of the latest set ofcollected web pages. The output may be the data uploaded to alarge-scale (but highly organized) database. The output may be generatedby repeating the following process: 1) find a page, 2) parse the pagefor info, 3) match the page to an object in the database, and 4) updatethe database.

Another computer fleet may be deployed to organize the content. As notedabove in the case of retrieval computers, content organization computersmay be replicated by building them from scratch programmatically. Eachcomputer is resurrected from a disk image, such as an Amazon MachineImage (AMI). The AMI is loaded as an elastic computing node on Amazon'sEC2 (elastic cloud computing) or other service using standard librarieswritten in Java. The AMI is armed with everything that the computer willneed, including a Java runtime environment, the capacity to communicatewith a central version control repository such as Git, etc. The AMI isalso armed with a startup script that runs when the EC2 node is born,and receives user parameters passed to the EC2 node at birth. The userparameters to the startup script tell it where to download the latestcode instructions for the node, such as the URL of an S3 location, orthe URL of a Git repository. The startup script is armed with thecredentials to access the latest code instructions, and load the codeonto the new EC2 node. Every EC2 node in the fleet downloads similarinstructions, so they are all prepped around a common task.

Every computer in the Content Organization fleet receives 2 pieces ofinformation (which it is programmed to seek out using in its bootinstructions): 1) the storage space location of the code instructions tobe its brain, 2) the location address of the job queue where it willreceive the material to be processed. The system controls the ContentOrganization fleet by creating, and managing the content organizationprocess. The system defines the storage directory of all the pages thatneed to be organized. The system thus turns this directory into a listof jobs, where each job is a file to be processed. The system thencreates a task queue (see below), loads that queue up with the tasks,and sets the properties of the queue to determine the time allotted fortask completion before tasks are recalled and given to other computers.

The task queue may be implemented using Amazon Simple Queue Service(SOS) or some other service that is external to individual computers.The system loads up the job queue with a list of pages that need to beorganized. Each item in the queue is a URL address in global storagespace to a page that needs to be organized. The goal is to distributethose URLs among the many computers. The queue allows computers to takeURLs, and retains a memory of which URLs still must be organized. Eachcomputer in the fleet can query the queue for the next item to becrawled. The queue then assigns the item to the computer, and marks theitem as “locked” so that other computers do not also try to work on theitem. Meanwhile, the system monitors the queue to determine whether thecomputer completes the task in a timely manner. If the computer does notindicate to the queue that the task is done within the allotted time thequeue restores the item to “unlocked” so that other computers can takethe task. Once a computer checks back with the queue to say that it hascompleted the task, the queue removes the task from the queue. Thus, aworkflow is established that can be shared between an arbitrary numberof computers where they can operate simultaneously to work through alist of retrieval tasks.

The global storage system for the Content Collection fleet may be adistributed storage platform (Amazon S3, etc.). In the case a Amazon S3,data is stored in buckets that are accessible from any computer as aURL. Each retrieval computer stores the contents of the retrieved filein a repository folder on S3 (or other service) as a filepath stringwhich is also URL. The file can thus be retrieved at a later date byentering the storage system URL. Access to these repository folders isrestricted so that they can only be accessed by the system's ContentCollection, and Content Organization systems.

The system may utilize the following global structure for documentnamespaces: date_retrieved/data_format/contentprovidericity/categord.For example: 2011-07-07/xml/google/boston/restaurants/. However,depending on the source of the crawl, the raw data files may not even beorganized into this directory structure yet. In this case the crawlresults should be sorted into files that are organized according to thisstructure.

To sorting raw crawl results, the system first inspects all the filesretrieved during Content Collection and sort them according to theobjects that they represent. One way to do so is inspect the URL of thecrawl. The URL will disclose the content provider, the city/metro area,and category. For sites where this cannot be computed from the URL, thedata can be extracted from elsewhere in the file (address field, etc.)The date of the crawl can be retrieved from the stored file's metadata.The crawl result file (or part of the crawl result file) that applies tothe extracted object can then be saved in the directory structuredescribed above. In this manner, all of the raw crawl results are placedin an organized directory structure to facilitate the subsequentorganization to the database.

The queue is loaded by accessing the storage system directory where thesorted documents are located (see above). The system then spiders thisdirectory to uncover the list of all files within that directory and itssub-directories. The system then creates a job queue (described above)to hold the list of files to parse. Next, the system uploads to thequeue a list of file locations (URLs to the files), as an array ofmessages, to the queue. At this point the queue is loaded with a set offiles to be parsed and organized.

Every time a computer in the fleet goes to the queue and retrieves asorted page to organize, it first analyzes the following informationfrom the URL: the “data format”, which determines how to read the file'sdata; the “content provider”, which determines which page parser toapply; and the “category”, which determines what type of object toextract. The computer already has in its memory all of the differentparsers that it downloaded when it was deployed. The computer picks oneout based on the content provider and data format, and runs it on thefile. Input is the file itself and the output is a data object in memorywith values extracted from the file and stored in fields.

Every time a computer parses a file, and stores its data object inmemory, the data is next added to the database. First, the computer hasto identify the object's location in the database. This is accomplishedby selecting the database table (in Amazon, a domain) based on thecategory of the object, and locating the row of the object by using, indescending order: i) the unique id of the object from the contentprovider (for example, restaurant id on local.yahoo.com), ii) anotherunique numerical identifier, such as the phone number, and iii) name,address, and latitude/longitude fuzzy matching. If the determined entrydoes not already exist, the computer creates a new row. The computerthen runs an update on that row, updating every attribute (field) in asingle database hit for efficiency. This is repeated for every sortedpage that the computers come across in the queue, until all of thesorted pages have been organized into the database.

Next, the system personalizes the content by generating a neural networkarchitecture that connects objects in the world as nodes within anetwork. The system activates a subset of the nodes based on what isknown about the user's affinities. The activations are followed throughthe network to deduce what else the user will like.

The neural network may be implemented as follows. Connections TO a nodea stored as a list of {N1, W1, N2, W2, . . . } where the connected nodesN are paired with their weights W. This list is saved in the database inthe same row as the other properties of the node. Optionally, a list ofconnections FROM the node can also be stored. Subsets of nodes to beactivated are identified by user-provided data regarding likes anddislikes. Users may be required to answer regarding their “favorites” indifferent categories. Users may also provide feedback on recommendationsthat they are given, which can be either binary (approve or disapprove)or they can be continuous (e.g., 1 to 10, or −10 to 10). The systemassembles a list of “positive activation nodes” and assign an activationlevel, which were either favorites (e.g., 10× activation) orfeedback-driven (e.g., 1-10× activation). Similarly, the systemassembles a list of “negative activation nodes” and assigns anactivation level (e.g., −1× to −10×).

Connections are established by, for every node in the user's list,accessing in the database the set of common co-occurrences with thatobject on the web. The system retrieves this list of objects and buildsconnections from our node to those objects with five positive synapseseach.

Connections also may be based on feature similarity. For every node inthe user's list, the system identifies nodes with similar properties.For the category to be matched, the system takes the most salientproperties (e.g., for a restaurant, price, cuisine and ambience) andsearches the database for other restaurants that match that feature set.Each match generates two positive synapses.

Connections also may be established based on cross-visitation. For everynode in the user's list, the system identifies nodes that have beencross-visited by other users. These users can be users of the system(e.g., users of a subscription service associated with the system) oractivity elsewhere on the Internet about which the system has data. Thismay be accomplished by indexing the reviews and responses to all nodes.The system identifies strong responses to the node of interest,identifies the users that furnished those responses, and identifiesother nodes to which those users had similarly strong responses. Thesystem can connect those nodes to our node of interest, with onepositive synapse for every similar response.

Negative synapses can facilitate the recommendation process by factoringin what the user does not like and the things that are not like thingsthat the user does like. Both of these associates involve negativesynapses, which add richness to the representation. For example, thesystem can identify strong responses to the node of interest, identifyusers that made those responses, and identify other nodes to which thoseusers had opposite strong responses. Alternatively, the system canidentify nodes that the user did not like, identify other people who didnot like that node, identify nodes that those people did like andpositively link those nodes to our user's preferences.

Sometimes the network may exhibit “runaway connectivity” where somethinggets more connected, which then gives it an advantage in getting furtherconnected (e.g., more co-occurrences) which in turn tends to generateeven further connections. Therefore the system may normalizeconnectivity by inspecting the list of existing connections to a node,determining their total value (e.g., # connections N×average weight W),and in the event that total value exceeds some threshold, divide all ofthe connection weights by a constant value to bring them back intorange. This may be repeated for all nodes. Normalization alternativelycan be accomplished by dividing based on the N*W term going TO the node,dividing based on the N*W term coming FROM the node, dividing by thetotal N*W term across the network. The implementation for this mayinvolve reading the list of node weights in the database, performing thenormalization on those weights, and writing the new weights back to thedatabase.

The addition of a new synapse connecting nodes can also immediatelyimpact other connections. Upon adding the connection to the list, theother connections to that node can be “taxed” by an amount equal to theinverse of their proportion of the new connection's strength—that is,adding a +1 synapse then taxes the other 10 synapses already on thatnode by 1/10=0.1. When synapses become so weak that they are below acertain threshold (either through interaction taxing or throughnormalization), then they are removed (deleted from the list).

Connections from node to node can be constantly analyzed, updated andconsolidated to take into account patterns that emerge between nodes. Asa simple example, if A forms a strong link to B, and A forms a stronglink to C, then a connection can be consolidated linking B and C. Suchpatterns can be searched for using specialized scripts that check thedatabase entries for such patterns, and then write back consolidationchanges to the affected nodes' lists.

The result of all of these processes is a rich information base thataccurately links a huge variety of nodes to a user's established nodesof interest, with a significant dynamic range, and with substantialretrieval efficiency.

To retrieve the list of nodes related to a user, the system need onlythen “activate” the user's established nodes, and follow theirconnections to retrieve more nodes that if connected sufficientlystrongly will also activate, and depending on the initial activationstrength follow those connections to further nodes until the activationpeters out with each connection hop depending on the connectionstrength. The connection strength is therefore the inverse of theresistance to the propagation of the activation through the network.

The total list of nodes that was effectively activated by this process(recommendation set) can then be stored in a list that is linked to theuser in the database, for retrieval with a single database callwhereupon the list can be cross-referenced against a set of presentedresults. Optionally, different sub-lists can be stored for differentcategories, or different presentation scenarios, caching the results forfast personalization.

The user interface may comprise i) a set of HTML files that define thelook and feel of the web interface, with design elements styled usingcascading style sheets (CSS), ii) a server-side set of scripts thatdynamically generate those HTML files using a backend scripting language(PHP, etc) running on a web server (Apache, etc.), iii) a client-sideset of scripts and interface libraries that allows rich user interactionwithin the browser (Javascript, jQuery, etc.), and iv) a backenddatabase that provides the data to the web application (Amazon SimpleDB,etc.).

The functionality of the user interface includes permitting the user tocreate an account and log in using secure credentials that are verifiedagainst an encrypted user table in our backend database. The interfacealso allows a user to browse objects and see whether they arerecommended or not. The interface allows a user to filter those objectsby city, by category, and then by a host of properties pertinent tothose categories. The user can enter feedback on their recommendationsby clicking on thumbs up/thumbs down or other feedback mechanisms. Theinterface allows a user to drag and drop recommendations onto a “beingconsidered” area where they can be compared across different parametersusing sortable headers, etc. The interface allows a user to drag anobject onto their calendar in order to “action” it by going to theobject at a certain time. The interface allows a user to build events,such as “My New York City Trip” where the user can create a group ofrestaurants, hotels, and other opportunities that have been recommended.The user can enter notes about their recommendations to remindthemselves of various impressions, for example. The user can print out acopy of itineraries for their events, or email those itineraries tothemselves. Their calendar is also synchronized with the global calendaron their smart phones, etc. The user can share their recommendationswith others, or build events and share those with others.

The interface may be delivered via a scalable cloud architecture. Webservers run as Linux CPU nodes on Amazon's elastic cloud computing (EC2)system. Web servers receive independent IP addresses using Elastic IP orother IP address mediators. Web servers are monitored for load, andusers are dynamically distributed among the servers. Excessive user loadtrips a threshold which leads to the creation of more EC2 nodes. Whenuser load drops too low, that trips a threshold which leads to thedelete of EC2 nodes to save cost.

A list of all recommended objects is pre-computed for the user. When tieuser requests objects via the interface, the system simply checks to IDsof those objects prior to presentation to see whether the objects appearon the recommended list or not. In another iteration, thepersonalization is computed in real time with no pre-cached list ofrecommended objects. In this example, as objects were going to bepresented through the interface, they are run through thepersonalization engine at that moment to compute if they are recommendedor not.

In some examples, the server and/or client device (e.g. desktop computeror smart phone) are implemented in digital electronic circuitry, or incomputer hardware, firmware, software, or in combinations of them. Theapparatus is optionally implemented in a computer program producttangibly embodied in an information carrier, e.g., in a machine-readablestorage device or in a propagated signal, for execution by aprogrammable processor; and method steps are performed by a programmableprocessor executing a program of instructions to perform functions ofthe described implementations by operating on input data and generatingoutput. The described features are optionally implemented advantageouslyin one or more computer programs that are executable on a programmablesystem including at least one programmable processor coupled to receivedata and instructions from, and to transmit data and instructions to, adata storage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that are optionallyused, directly or indirectly, in a computer to perform a certainactivity or bring about a certain result. A computer program isoptionally written in any form of programming language, includingcompiled or interpreted languages, and it is deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory are optionally supplemented by, or incorporatedin, ASICs (application-specific integrated circuits).

To provide for Interaction with a user, the features in some instancesare implemented on a computer having a display device such as an LCD(liquid crystal display) monitor or screen for displaying information tothe user and, in the case of a desktop computer, a keyboard and apointing device such as a mouse or a trackball by which the userprovides input to the computer.

In various implementations, the client device is a smart phone such asthat described in U.S. Pat. No. 7,966,578, entitled “PortableMultifunction Device, Method, and Graphical User Interface forTranslating Displayed Content,” assigned to Apple, Inc., which isincorporated herein by reference.

The server functionality described above is optionally implemented in acomputer system that includes a back-end component, such as a dataserver, or that includes a middleware component, such as an applicationserver or an Internet server, or that includes a front-end component,such as a client computer having a graphical user interface or anInternet browser, or any combination of them. The components of thesystem are connected by any form or medium of digital data communicationsuch as a communication network. Examples of communication networksinclude, e.g., a LAN, a WAN, and the computers and networks forming theInternet.

The computer system optionally includes clients and servers. A clientand server are generally remote from each other and typically interactthrough a network, such as the described one. The relationship of clientand server arises by virtue of computer programs running on therespective computers and having a client-server relationship to eachother.

Error Correction and Data Verification

In selected embodiments, the recommendation engine 112 generatesrecommendations for venues based on a variety of information such asuser data, venue data and reviewer data. More specifically, the userdata, venue data and reviewer data are combined as previously describedherein to form link matrices the strength of which can be used togenerate the recommendations for the user. However, whilerecommendations based on link strengths between nodes of the neuralnetwork provide a strong gauge as to the accuracy of the generatedrecommendations, it is possible that nodal link values can “trick” therecommendation engine 112 into generating an outlying recommendation forthe user.

For instance, a neural net configuration having nodal link strengthsstrongly geared to specific data such as venue attire, genre and priceas well as reviewer data positively identifying venues having thesetraits may recommend a venue in a neighborhood that is quite differentfrom the neighborhood where the user normally eats dinner. The system100 may also strongly link user attribute data such as work hours andprofession to venues having corresponding business hours and attire suchthat the recommendation engine 112 recommends an exorbitant venue thatis drastically outside the price range of venues the user typicallyfrequents based on past recommendations. Therefore, while therecommendation engine 112 will typically generate a recommendation sethaving a plurality of accurate recommendations, it is possible due toparticular nodal links that the recommendation engine 112 may generatean outlying recommendation that does not “resonate” with the previousrecommendations served to the user.

Referring back to FIGS. 3-8, an illustrative example of this effect isprovided wherein the recommendation engine 112 generates arecommendation for a venue that is in a neighborhood the user does nottypically visit or typically receive recommendations to visit.Referring, for the purposes of this example, solely to the affinityexpressed by Reviewer 7, there is a +0.75 collaborative-based linkformed between Restaurant 7 and Restaurant 11. Further, based on thevenue attributes themselves, there is a +0.25 rating as both Restaurant7 and Restaurant 11 have the same attire. Additional ratings could beadded therebetween as described previously based on similar hours andthe fact that the price points of the Restaurant 7 and Restaurant 11 aresimilar. Therefore, the nodal link strength between Restaurant 7 andRestaurant 11 would be quite strong upon the formation of link matricesby the Matrix Builder 126 such that the recommendation engine 112 maygenerate a recommendation for Restaurant 7 in response to receiving aquery of Restaurant 11 by the user via the user interface. However,assuming for the purposes of this example that previous recommendationsto this user were typically in neighborhood 02196 and/or rarely, ifever, involved Japanese food, the recommendation engine 112 would beproviding a recommendation of Restaurant 7 that clearly does notresonate with recommendations previously made to the user.

To prevent the risk of an erroneous recommendation, one exemplaryembodiment of the system 100 provides processing for error correctionand data verification via the recommendation engine 112. Through thisimproved processing, the recommendation engine 112 can providerecommendations that correlate strongly to the content-based,collaborative-based and content-collaborative based interrelationshipsand that resonate with the plurality of recommendations that werepreviously made to the user. Accordingly, the error correction and dataverification acts as a guardian against recommendations that, althoughbased on strong nodal links of the neural network topology, do notresonate with recommendations that were previously served to the userand acted upon by the user.

The error correction and data verification processing begins, in oneembodiment by storing in the data repository 118 recommendations datathat was previously generated by the recommendation engine 112 andserved to the user. The system 100 may designate a minimum number ofrecommendations that have to be stored before error correction and dataverification processing is implemented but may also impose limits on thenumber of stored recommendations based on storage capacity andprocessing considerations. The recommendations data stored in the datarepository 118 can in selected embodiments store not only therecommended venue itself but also the user data, venue data and reviewdata that was considered most pertinent to the recommendations generatedby the recommendation engine 112. Therefore, the data repository 118 canstore a recommended venue in relation to the data values ascribed withthe recommended venue itself such as genre, hours of operation, attire,neighborhood and any other value described herein or as would beunderstood by one of ordinary skill in the art. The data repository 118can also store any other data strongly relied upon by the recommendationengine 112 when generating the recommendation such as reviewerinformation, content-based link values, collaborative-based link valuesand user attribute data such as age, education and profession.

FIG. 18 illustrates an exemplary data repository 118 storing previousrecommendation data for Users 2, User 4 and User 7. This example is ofcourse non-limiting as the data repository can contain more entries aswell as different types of recommendation data previously describedherein and as would be recognized by one of ordinary skill in the art.Further, although the recommendation engine 112 can provide arecommendation set to the user consisting of a plurality of recommendedvenues, it is assumed for he purposes of the examples provided by FIG.18 that each recommendation set provided to each user contained only oneitem of recommendation data. As it may be difficult to detectrecommendation resonate outliers with a limited amount of priorrecommendations, the system 100 in selected embodiments can set aminimum number of recommendation data that is required for each userbefore the recommendation data can be relied upon for performing errorcorrection and data verification processing. Therefore, the system 100may not perform error correction and data verification processing untila predetermined threshold quantity of recommendation data has beenstored in the data repository 118 for the particular user. Thispredetermined number relating to initiation of error correction and dataverification processing may be manually set by the user via the userinterface or automatically set by the system 100.

In FIG. 18, each user is stored in relation with recommendation datathat has been previously recommended by the recommendation engine 112.For example, the first recommended venue for User 2 contained data suchas a price point value of 3, an Italian genre, casual dress attirerequirements and location neighborhood 02196 data. Further, therecommendation data stores the reviewers that reviewed the recommendedvenue (Restaurant 2) and user attribute information such as the age atwhich the recommendation was made and the number of children the userhad at the time the recommendation was made. Therefore, therecommendation data stores values that will change over time such as thenumber of children and the age of the reviewer and user. Although notillustrated, it is further expected that venue attributes values such asthe price point value and neighborhood and reviewer affinity ratings maychange over time.

FIG. 19 illustrates an example of aggregate repository recommendationdata stored in the data repository 118 according to selected embodimentsthat is generated based on the data repository of previousrecommendations illustrated in FIG. 18. In one embodiment, the system100 collates the recommendation data for each user to determinestatistical values for each item of information stored in the datarepository 118 relating to previous recommendations. For example, it canbe seen that User 2 has received three recommendations all of which wereItalian Venues. Further, the aggregate data indicates that therecommended venues to User 2 were often at the lower price point rangeas User 2 received two recommendations at a price point of three and onerecommendation at a price point value of two. Further, FIG. 18illustrates that User 2 has always received recommendations forneighborhood 02196. Therefore, as discussed previously and explainedfurther below, a recommendation set for User 2 including venues havinghigh price points and/or venues in a neighborhood outside of 02196 maynot resonate with the aggregate data stored in the data repository 118.

The system 100 can determine the aggregate recommendation data based onthe previous recommendations stored in the data repository 118 in realtime when making a recommendation and performing error correction anddata verification. In other selected embodiments, the system 100 canaggregate the previous recommendations at a time when the system 100 isexperiencing a lower than normal processing load and store the aggregatedata as illustrated in FIG. 19. Accordingly, the system 100 has theoption of determining the latest aggregate information by aggregatingthe data at the time of performing error correction and dataverification processing or can lower the processing load required forerror correction and data processing by aggregating the storedrecommendation data at periodic or predetermined intervals.

Whether the aggregated data is collated simultaneously with the issuanceof a recommendation or at a previous time, the stored recommendationscan be aggregated in a variety of ways. For example, in one embodiment,the system 100 determines a quantification value for each attribute ofthe recommendation data which can then be used in the error correctionand data verification processing. An illustration of this methodology isshown in FIG. 19 wherein quantification values for the price of thevarious venues previously recommended to User 2 are stored separatelysuch that a price point of “3” was provided twice by the recommendationengine 112 and a price point of “2” was provided only once by therecommendation engine 112. As the data collected from venues, reviewersand users is likely to change over time, the aggregated data can also becollated based on running averages. FIG. 19 provides an example of thistype of collation wherein the ages of the reviewer has changed based onthe time at which previous recommendations have been made and thereforean average age value is provided in the aggregate data. Therecommendation engine 112 may also limit the error correction and dataverification processing to a certain number of recommendations toprevent out-of-date data from affecting resonate recommendationcomputations. As such, in selected embodiments, the data repository 118can store a time stamp with each data recommendation entry such that therecommendation engine 112 can implement temporal filter values whenperforming error correction and data verification.

The system 100 can therefore adapt over time to the changing affinitiesof users and reviewers alike. For example, recommendations to along-time system 100 user may have originally all been tailored tovenues at a low price point with casual attire whereas the user is nowolder and mostly frequents venues at a higher price point with formalattire. However, the introduction of children into the user's lifestylemay then shift recommendations back to a lower price point. Reviewerratings will most likely also change over time and therefore therecommendation engine 112 can reflect only the most recent and accurateratings when performing error correction and data verification.

The previous recommendation data illustrated in FIG. 18 and FIG. 19 mayalso be weighted based on affinities explicitly expressed by a user suchthat the recommendation engine 112 provides more emphasis on specificattributes of data when performing error correction and dataverification. For example, User 4 may indicate via the user interfacethat he prefers Japanese and Italian venues and venues in theneighborhood 02163. Therefore, when the recommendation engine 112performs error correction and data verification it will determinewhether the current recommendation resonates with the various aggregateddatum of previous recommendation data but will also place particularemphasis on verifying that the current recommendation resonates withUser 4's affinity for Japanese and Italian venues as well as venues inthe neighborhood 02163. It is likely that the recommendations willalready be tailored somewhat towards these feature sets based on thenodal links formed by the matrix builder 126 based on user-expressedaffinity but the error correction and data verification processingprovides an extra filter of protection to ensure a more accuraterecommendation to the user.

Weights may also be provided to the aggregate data of previousrecommendations based on the “primary” nodal link strengths of theneural network used by the recommendation engine 112 to generate theprevious recommendations. For example, based on content-basedinterrelationships, collaborative-based interrelationships,content-collaborative interrelationships, and higher-orderinterrelationships, the recommendation engine 112 may generate arecommendation primarily based on genre data and neighborhood data. FIG.18 provides an illustrative example wherein all of the previousrecommendations to the User 2 have been for an Italian venue within theneighborhood 02196. Therefore, the recommendation engine 112 may attachweights to these values such that they provide more influence onresonation calculations via the error correction and data verificationprocessing as described above.

FIG. 20 is a flow chart illustrating error correction and dataverification processing according to one embodiment. The process isinitiated at S2000 with the recommendation engine 112 determining arecommendation set based on the neural network methodology describedabove. Processing then proceeds to S2002 to perform a comparison of thecurrent recommendation data generated by the recommendation engine 112with the aggregate recommendation data stored in the data repository118. Although not illustrated in FIG. 20 but as previously discussed,the recommendation engine 112 can determine whether to initiate errorcorrection and data verification based on the number of recommendationsfor a particular querying user that are stored in the data repository118.

Assuming the recommendation engine 112 has access to the requisiteamount of aggregate recommendation data, the recommendation engine 112compares the current recommendation to the aggregate recommendation datastored in the data repository 118. In one embodiment, the recommendationengine 112 systematically compares each attribute included in thecurrent recommendation data to each corresponding aggregatedquantification value of each attribute to determine a resonate value forthat attribute. Once each resonance value is determined for each of theattributes corresponding to those contained in the currentrecommendation, a resonance quantifier is determined based on theplurality of resonance values. If the resonance quantifier is less thana predetermined threshold, then the recommendation engine 112 determinesthat the current recommendation does not “resonate” with previousrecommendations at S2004. Accordingly, at S2004, if the resonantquantifier is greater than a predetermined threshold, the recommendationengine 112 confirms that the current recommendation “resonates” withprevious recommendations.

If the recommendation engine 112 determines at S2004 that the resonancequantifier does not exceed the predetermined threshold and thereforethat the current recommendation does not resonate with previousrecommendations to the user, the recommendation engine 112 identifiesthe deficiencies in the current recommendation such that the matrixbuilder 126 can back propagate these deficiencies into the neuralnetwork to establish increased accuracy within the nodal links. Forexample, with reference to FIG. 18 and FIG. 19, if the recommendationengine generated a recommendation for a venue in a neighborhood outside02196, the error correction and data verification processing mayindicate this recommendation as an outlier based on previousrecommendations for User 2 that were all within the neighborhood 02196.Accordingly, at S2012, the neural network is updated such that anegative link value is ascribed to the neighborhood identified in therecommendation with respect to the nodal links established for User 2.Alternatively, or in addition, the matrix builder 126 may ascribe anadditional positive link value to the neighborhood 02196 to furtherensure that neighborhood 02196 is given increased link strength therebyensuring increased consideration in any future recommendations providedby the recommendation engine 112. Accordingly, the error correction anddata verification processing not only provides an additional filter foraccurate recommendations but also acts as a vehicle for drivingincreased accuracy within the nodal links of the neural network itself.After updating the neural network at S2012, the processing proceeds backto S2000 for the recommendation engine 112 to generate a newrecommendation for the user based up the updated neural network.

Referring back to FIG. 20, once it is determined that the currentrecommendation resonates with previous recommendations at S2004, thecurrent recommendation is provided to the user at S2006 and the datarepository 118 is updated to include an entry for the currentrecommendation. As noted above, this entry may include a time stampindicating the time and date at which the recommendation data wasentered into the data repository 118. After updating the data repository118 with the current recommendation, processing proceeds to updating theneural net nodal links at S2010. As the system 100 has determined thatthe recommendation data generated by the recommendation engine 112resonates with previous recommendations, the matrix builder 126 mayupdate the nodal link strengths of the neural network based on dataincluded in the recommendation data. For example, if the recommendationdata for User 2 includes casual attire for the recommended venue, thematrix builder 126 may even out the nodal link strengths with respect tothis attribute as User 2 has now been recommended the same amount ofvenues for both casual and formal attire. Further, this recommendationdata “approved” by the error correction and data verification processingmay be applied more weight when updating the neural network thanrecommendation data that was created before error correction and dataverification processing was initiated by the recommendation engine 112as it has confirmed resonance with previous recommendations and istherefore more likely to be accurate. Further, in addition to themethods of refining the neural network based on the effectiveness ofrecommendations as determined by the system 100, a recommendation thatis approved by the error correction and data verification processing mayalso be identified as a recommendation that has been determined to beeffective based on the resiliency of the aggregate previousrecommendation data. Further, in selected embodiments, when the system100 determines the effectiveness of the recommendation data as describeherein based on financial data, feedback data, web browsing data,geographic data or the like, the system 100 may store thisrecommendation data in the data repository 118 with a special label suchthat the recommendation engine 112 applies more weight to thisinformation when performing error correction and data verificationprocessing. For example, in referring to FIG. 18, if the system 100determines the effectiveness of recommendation #2 by receiving financialinformation that User 2 visited the venue, by receiving user feedbackfrom User 2 that he liked the venue, by determining that User 2 spends alot of time on the venue website, or by determining that User 2 spends alot of time in a geographic location proximate to the recommended venue,the system 100 may apply a “validation” label to this recommendationdata in the data repository 118. In doing so, if the recommendationengine 112 is using only a predetermined number of previousrecommendations, the recommendation engine 112 can improve errorcorrection and data verification accuracy by using only “validated”previous recommendations. In other embodiments and if the recommendationengine 112 is using more previous recommendations than currentlyavailable validated previous recommendations, the recommendation engine112 may apply special weight to the validated previous recommendationssuch that the error correction and data verification processinggenerates a resonance quantifier that is more strongly influenced bypreviously validated recommendations.

While error correction and data verification provides enhanced accuracywith respect to recommendations to the user, the system 100 must beconstantly vigilant with respect to the changing attributes within therecommendation data, particularly the user attribute data. For example,if the user moves to a different part of the country then the matrixbuilder 126 will update the neural network accordingly thereby loweringthe strength of links identifying venues in neighborhoods that are nolonger in proximity to the user. Therefore, the nodal link strengthswill be reflected in a new recommendation such that any recommendationby the recommendation engine 112 will be in neighborhoods in closeproximity to the user's new location. However, when comparing a newrecommendation to this user, the recommendation engine 112 may identifythis recommendation as an outlier as all previous recommendations werein neighbors geographically distant from the user's new location.Accordingly, the recommendation engine 112 may assign a label on theneighborhood attribute data within the data repository 118 of previousrecommendations thereby identifying certain values of this attribute asdata which should not be used when performing error correction and dataverification. Therefore, the system 100 provides adaptive functionalityto limit error correction and data verification processing to attributesthat will not induce the recommendation engine 112 to provide erroneousresults.

Another method for providing enhanced recommendations via errorcorrection and data verification is to dynamically ensure resonancebetween information, such as review data, from various source sites thatare used during harvesting operations. For example, the harvesting ofdata to create the neural network as previously described herein obtainsinformation from a variety of sources such as web sites. However, as websites are constantly changing, as well as user review data and venuedata contained therein, the system 100 may not always have an up-to-datecapture of information. As described previously, the system 100periodically updates the neural network based on information gatheredfrom source sites. However, the user may perform a search query at atime before an update but after information from the source sites haschanged. Accordingly, nodal link strengths of the neural networktopology, while providing a strong representation of links betweendifferent venues, may not be optimized as they were determined based oninformation that was “out of date”. Therefore, error correction and dataverification further provides the ability to dynamically determineresonance between information contained within the data repository 118and information obtained from source sites as well as betweeninformation from different source sites.

Specifically, in selected embodiments, the recommendation engine 112will, just prior to generating a recommendation set, dynamically harvestdata items from multiple source sites, such as web sites across theInternet, and resolve any differences between these data items. Forexample, venue data items from a majority of web sites relating toRestaurant 1 of FIG. 4 may include information identifying Restaurant 1as low cost and casual. However, it is possible that other websites maymistakenly indicate Restaurant 1 as expensive and requiring formalattire. Therefore, the recommendation engine 112 can resolve thesedifferences by indicating certain web sites as containing inaccurateinformation with respect to a venue. Once the outlying information isobtained, the neural network is dynamically updated as previouslydescribed herein while excluding the outlying information therebyallowing the recommendation engine 112 to provide a more accuraterecommendation set based on new overall link strengths of the updatednetwork topology.

In selected embodiments, a sliding scale resonance threshold may beapplied to determine at what point information can be deemed accurate asopposed to outlying with respect to other harvested information. Forexample, the scale may be set by the system 100 or a user such that 75%of the information harvested must conform in order to determine thatinformation not conforming with information in the 75^(th) percentile isoutlying information. The system 100 may further adjust the thresholdbased on the amount of web sites containing information about a certainvenue. For example, the more information with respect to the same dataitems that the system 100 can obtain during harvesting operations, thehigher the threshold may be set as there is a large data set form whichto draw accurate information. Accordingly, if the system 100 harvestsinformation from 100 sites of which 65 indicate Restaurant 1 as low costand casual and 45 indicate that Restaurant 1 is expensive and formal,the system 100 may determine that the 45 web sites indicating Restaurant1 as expensive and formal are outliers and will not take theirinformation into account when updating the neural network. However, ifthe system 100 harvests information from 1000 sites of which 650indicate Restaurant 1 as low cost and casual and 450 indicate Restaurant1 as expensive and formal, the system 100 may not identify the 450 itemsof Restaurant 1 venue information as outlying information as there is alarger data set of which 65% is not a high enough resonance threshold.In this instance, the system 100 may opt to use the informationcontained within the 650 web sites but provide a negative weighting tothis information so that it does not have too much of an effect upon anupdate of the neural network topology. Alternatively, the system 100 mayignore all of the information and determine that it cannot be resolvedand therefore that none of it should be used to update the neuralnetwork topology.

Once error correction and data verification has been dynamicallyperformed at the time of a user query, the neural network topology isupdated and the recommendation engine 112 provides a recommendation setbased on a current real-time snapshot of information contained withinthe Internet. Accordingly, information provided to the users is asaccurate as possible with respect to information harvested from theInternet. Further, this allows the system 100 to determine “trendiness”data among web sites such as certain attribute data which is likely tochange rapidly. This provides the system 100 with the ability toidentify various types of fickle data and provide appropriate weightswhen calculating nodal links strengths within the neural network so thatthe user receives recommendation sets based on the most stable andaccurate information harvested by the system 100.

Geometric Contextualization

As discussed above, error correction and data verification provides thesystem 100 with a way to avoid outlying recommendations as well as a wayto monitor venue, reviewer and user attributes that change over time.However, in addition to these gradual characteristic changes over time,users are often inclined to change their interests spontaneously to trysomething new or simply to see what options the system 100 may producein response to queries containing a variety of user filters. Forexample, the user may express an affinity for a particular venue but maywant to further limit the search to venues that have particular hours,attire and neighborhood requirements. Accordingly, in selectedembodiments, the recommendation engine 112 must generate a plurality ofrecommendation data having venues with the strongest link strength tothe venue provided by the user but which are also limited to the hour,attire and neighborhood requirements. Of course, the more filters theuser provides with a search query, the harder it is for therecommendation engine 112 to generate a good recommendation that meetsall of the requirements of a user. For example, if a user queries thesystem 100 for recommendations within the state of Massachusetts, therecommendation engine 112 may not recommend certain venues fromSalisbury, Mass. based on weak nodal link strengths even though they arelocated in the state of Massachusetts. As such, in selected embodiments,the recommendation engine 112 determines a plurality of venuerecommendations based on nodal link strengths and then compares thenodal link strength of each recommendation to a recommendation thresholdto determine whether or not the venue should be recommended to the user.The recommendation threshold indicates a watershed overall link strengthvalue or in other selected embodiments a percentage identifying thenumber of recommendations that will be recommended by the recommendationengine 112 to the user out of the recommendation set. Accordingly, forexample, if in response to a particular user query the recommendationengine 112 generates ten venues based on the above-described datainterrelationships, the recommendation engine 112 may only serverecommended venues having nodal link strengths exceeding therecommendation threshold such that only 3 out of the 10 recommendedvenues are served to the user.

However, if the user then performs the same query but limits thegeographic limitations of the search to Salisbury, Mass., or aneighborhood of Salisbury, Mass., and the recommendation engine 112 onlydetermines a few recommended venues based on the nodal link strengths,the recommendation engine 112 may not recommend any venues if none ofthe nodal link strengths are greater than the recommendation threshold.In this instance, the user would be served with a useless emptyrecommendation set thereby lowering user confidence in the system andincreasing the likelihood the user will turn to other systems forinformation.

FIG. 21A illustrates an exemplary search according to the above-notedprinciples by describing the outputs of the recommendation engine 112based on a query for a recommendation for an American restaurant withcasual attire and a user affinity for Restaurant 5. Based on this query,the recommendation engine 112 determines the venues having the strongestnodal link strength to Restaurant 5 while also limiting therecommendation set having these strong nodal link strengths solely toAmerican restaurants with casual attire. This example is limited to tworestaurants based on the data set illustrated in FIG. 4, but it isassumed that other restaurants may exist which are listed as RestaurantsX. For the foregoing examples, the value X may represent a small numberof restaurants or a large number of restaurants. In this example, aswith FIG. 9, the recommendation is a blending of at least thecontent-based link strength 2100, collaborative link strength 2104 andcontent-collaborative link strength 2108. Each link strength is assigneda distinct weighting factor 2102, 2106 and 2110. By referring to FIGS.4, 7, and 8, FIG. 21A provides respective values for content-based linkstrength 2100, collaborative link strength 2104 andcontent-collaborative link strength 2108. As Restaurant 3 has nocontent-based interrelationships or collaborative interrelationshipswith Restaurant 5, the content-based link strength 2100 andcollaborative link strength 2104 is zero. The content-collaborative linkstrength 2108 is exemplary as are the weighting factors 2102, 2106 and2110. Based on a first order sum of products, the overall link strengthfor Restaurant 3 is 0.075. Restaurant 6 has the same attire asRestaurant 5 and therefore has a content-based link strength 2100 valueof 0.25 but has a negative collaborative link strength 2104 value of−1.0 based on a strongly opposite affinity expressed by Reviewer 3. Aswith Restaurant 3, the content-collaborative link strength 2108 ofRestaurant 6 is exemplary as are the weighting factors 2102, 2106 and2110. Accordingly, in this example, a first order sum of productsproduces and overall link strength 2112 value of −0.1875. For the otherRestaurant(s) X, values of A-F are assigned, respectively, forcontent-based link strength, collaborative link strength,content-collaborative link strength and their corresponding weightingfactors. Further, Restaurant(s) X have an overall link strength of G. Asillustrated in FIG. 21A and described above, the result of theabove-noted query is Restaurant 3, Restaurant 6 and Restaurant(s) X.Therefore, assuming X represents a small number of restaurants having anoverall link strength less than Restaurant 3 and Restaurant 6, and basedon the filter state implemented by the user via the user interface atthe time of the query for recommended venues, the recommendation dataset for this filter state is very small. Further, the overall linkstrengths of Restaurant 3 and Restaurant 6 are low enough that therecommendation engine 112 may not recommend them to the user if they donot pass the recommendation threshold. For example, if therecommendation threshold is set at a value of 0.25, neither Restaurant 3or Restaurant 6 would be recommended by the recommendation engine 112and an empty recommendation set would be provided to the user.Accordingly, as noted above, while the recommendation threshold iseffective for reducing a large recommendation data set, it can also actas a barrier for passage of all recommendations when the recommendationset is extremely small and/or has small overall link strength valuestherein. Therefore, the system 100 must provide a way to avoid servingempty recommendation sets to users when the search returns a limitedrecommendation set having low overall link strength values. In otherwords, the system 100 must balance the need to provide information tothe user while also considering the value or relevance of theinformation being presented to the user such that user does not loseinterest or confidence in the system 100.

Geometric contextualization is a mechanism for overcoming the problem oflimited recommendation sets by ensuring that at least one recommendationis always provided to the user via the recommendation engine 112. Onemethod of performing geometric contextualization is to adjust theoverall link strength values of the recommendation set generated by therecommendation engine 112 until at least one of the recommended venuesexceeds the recommendation threshold. The recommended venues exceedingthe recommendation threshold can then be served to the user or apredefined percentage of recommendations out of the recommendation setthat have had their overall link strengths adjusted are served to theuser. In this embodiment, the overall link strength values of eachrecommendation are normalized using a normalization factor that is basedon various factors until the overall link strength value of at least onerecommended venue exceeds the recommendation threshold. Accordingly, inselected embodiments, geometric contextualization is performed everytime a recommendation a set is generated by the recommendation engine112 to further redefine recommendation rankings based on a variety offactors to enhance the accuracy of the percentage of recommendationsserved to the user. One factor that can be used to generate thenormalization factor for normalizing the recommendation set is thenumber of potential recommendations available based on the filter stateset by the user at the time of performing the query. For example, ifthere is a large number of recommendations available based on the filterset and the only issue is that none of the recommendations of the setexceed the recommendation threshold, a minimal normalization factor maybe utilized to normalize the recommendation set such that a limitedamount of recommendations exceed the recommendation threshold.Therefore, the recommendation engine 112 may generate a normalizationfactor to ensure that a predetermined number of recommended venuesexceed the recommendation threshold. This ensures that therecommendation engine 112 can serve the user with at least onerecommendation but increases the chance that the recommendationsprovided are the “best” recommendations out of the group based on theoverall link strength values. In other words, by having therecommendation engine 112 perform geometric contextualization with a lownormalization factor for a large recommendation set, only therecommendation values with the largest overall link strength will beprovided to the user. If the recommendation set is smaller in size, therecommendation engine 112 may need to generate a drastically differentnormalization factor to ensure that at least one recommended venue willbe normalized to a value exceeding the recommendation threshold based onthe nodal link strengths of the smaller set of recommended venues.

In selected embodiments for performing geometric contextualization basedon the number of recommendations available in the filter state, thenormalization factor value itself can be set by the recommendationengine 112 based on specific calculations with respect to therecommendation set. For example, the recommendation engine 112 cananalyze the recommendation data set and determine a normalization factorthat will ensure that the overall link strength of at least onerecommended venue exceeds the recommendation threshold. Further, thesystem 100 or the user via the user interface may set a specified numberof recommendations to receive for each query. Therefore, therecommendation engine 112 may calculate a normalization factor that willensure the overall link strengths of the specified number of recommendedvenues exceeds the recommendation threshold to ensure the user receivesthe requisite number of recommendations. In addition or alternatively,the recommendation engine 112 may set the normalization factor based onuser statistics known to the system 100 relating to venue attributes,user attributes, reviewer attributes, ordered relationships,content-based interrelationships, collaborative based interrelationshipsand/or content-collaborative interrelationships. These statistics aredetermined as described above with respect to determining theeffectiveness of recommendations. For example, if past recommendationsof the user indicate that recommendations based on content-based linkstrength are more effective than recommendations based on collaborativelinks strength, the recommendation engine 112 may calculate anormalization factor that ensures at least one recommendation havingstrong content-based link strength is elevated past the recommendationthreshold even if other recommendations having higher overall linkstrengths exist in the recommendation set. In this instance, therecommendation engine 112 may apply the normalization factor only tothose venues in the recommendation set that have a requisite level ofcontent-based link strength. Of course, this method may be applied basedon collaborative link strength, content-collaborative link strengthand/or higher order interrelationships. This provides the system 100with the ability to elevate those recommendations that have a loweroverall link strength but that may prove more effective for the userbased on previous user statistics. This enhances the users overallexperience with the system 100 and provides enhanced data for merchantvendors.

An example of geometric contextualization using a normalization factorbased on the number of potential recommendations is illustrated in FIG.21B with reference to FIG. 21A.

In this example, it is assumed that the recommendation engine 112 hasgenerated Restaurants 3, 6 and Restaurant X1 in response to the userquery identified for FIG. 21A. In this example, assuming arecommendation threshold of 0.25 and an overall link strength value Gthat is less than 0.25 for Restaurant X1, none of the overall linkstrengths 2112 exceed the recommendation threshold. Accordingly, thesystem 100 determines that the recommendation engine 112 needs toperform geometric contextualization on the recommendation data. Whenperforming geometric contextualization, the recommendation engine 112determines that there is a small number of recommendations (3) availableand therefore an adequate normalization factor must be calculated toensure that at least one of the recommended venues exceeds therecommendation threshold once the process of geometric contextualizationis finished. Accordingly, assuming a user specified or system 100specified recommendation limit of two, the recommendation engine 112must calculate a normalization factor such that two of the overall linkstrengths are elevated above the 0.25 recommendation threshold.Accordingly, a normalization factor of +0.175 is added to the overalllink strength 2112 value of each venue in the recommendation set. FIG.21B illustrates the effects of geometric contextualization on therecommendation data illustrated in FIG. 21A as discussed. In FIG. 21B,due to the effects of normalization, both Restaurant 3 and a RestaurantX1 out of the set of recommended venues have the requisite overall linkstrength of at least 0.25 to ensure they are higher than therecommendation threshold and can therefore be served to the user via theuser interface.

However, in other selected embodiments and as previously discussed, ifthe system 100 determines statistically that content-basedinterrelationships have often proven to be the most successful factor indetermining the effectiveness of the recommendation, the recommendationengine 112 may generate a normalization factor specific to venues havingthe highest content-based link strength thereby providing extra emphasisto the overall link strength of Restaurant 6 with respect to thepredetermined threshold. Standard normalization factors may then beapplied to the remaining venues in the recommendation set. Therefore, ifa strong enough normalization factor is applied specifically toRestaurant 6, Restaurant 6 may exceed the recommendation threshold uponcompletion of geometric contextualization. However, as Restaurant 6 hassuch a low overall link strength with respect to Restaurant 3 andRestaurant X1, the recommendation engine 112 may determine anormalization factor such that Restaurant 6 still does not exceed therecommendation threshold despite the previous user statistics withrespect to the content-based interrelationships. Therefore, therecommendation engine can perform geometric contextualization to avoidempty recommendation sets while taking into account previous userstatistics and balancing them against overall link strengths determinedfrom the nodal links of the neural network. Further, if, for example,the system 100 contains data strongly correlating a low venue pricepoint to the effectiveness of the recommendation, the recommendationengine 112 may only perform geometric contextualization onrecommendations having lower price points thereby elevatingrecommendations strongly relating to user-specific characteristics.Therefore, although not shown in FIG. 21B, the recommendation engine 112may only perform geometric contextualization on Restaurant 6 as it has alow price point with respect to Restaurant 3. Accordingly, in thisexample, even with a negative overall link strength, Restaurant 6 may beelevated above the recommendation threshold such that the user receivesa recommendation tailored to his characteristics.

The recommendation engine 112 may also perform geometriccontextualization based on the aggregate or individual quality of therecommendation data identified in the recommendation set. The quality ofthe recommendations in the recommendation set can also be determinedbased no the overall link strength between the recommended venues andvenues identified by the user in a search query. Further, in selectedembodiments, the quality of the recommendation data is determined byidentifying the effectiveness of the recommendation based on previousrecommendation stored in the data repository 118. As described above,certain prior recommendations may have a validation label if therecommendations have been determined effective based on user reviews,user financial data, user geographic data or other information aspreviously described herein. Further, the quality of the recommendationdata can be determined based on the recommendation effectiveness datapreviously described herein with respect to identifying user financialtransactions at the recommended venue, habitual user proximity to thevenue and so forth. Accordingly, assuming the recommendation engine 112generates a recommendation set having data for five recommendations thatare all below the recommendation threshold, the recommendation engine112 can search each recommendation data item to determine whichrecommended venues are most closely related to the recommendation datathat has previously been determined to be effective or have thestrongest overall link strength to venues identified in the searchquery. Based on this determination, the recommendation engine can thenperform geometric contextualization by determining a normalizationfactor that will elevate the requisite amount of recommendations fromthe “effective subset” above the recommendation threshold.

An example of geometric contextualization using a normalization factorbased on the aggregate or individual quality of the recommendations inthe recommendation set is illustrated in FIG. 21C with reference to FIG.21A. In this example, as with the previous example, it is assumed thatthe recommendation engine has generated Restaurants 3, 6, and RestaurantX1 in response to the user query identified for FIG. 21A. In thisexample, assuming a recommendation threshold of 0.25 and an overall linkstrength value G that is less than 0.25 for Restaurant X1, none of theoverall link strengths 2112 exceed the recommendation threshold.Accordingly, the system 100 determines that the recommendation engine112 needs to perform geometric contextualization on the recommendationdata. When performing geometric contextualization, the recommendationengine 112 determines that Restaurant 3 and Restaurant 6 are of higherquality based on the effectiveness of previous recommendations made bythe recommendation engine 112 that are stored in the data repository 118with a validation label. For example, the recommendation engine 112 candetermine from data stored in the data repository 118 that User 2 hasvisited Restaurant 8 numerous times based on financial transactions fromRestaurant 8 and further based on geographic habituations with respectto User 2 proximity to the neighborhood 02196. Accordingly, therecommendation engine 112 determines a quality factor for Restaurant 6based on similarities between Restaurant 6 and Restaurant 8 such ashaving an identical pricepoint as Restaurant 8. Further, therecommendation engine 112 may determine the effectiveness of Restaurant2 based on a multitude of positive review data from User 2 and furtherdetermine that User 2 often eats at venues having casual attire andlives in neighborhood 02199 such that a quality factor for Restaurant 3is calculated based on these considerations. Further, for the purposesof this example, it is assumed that the recommendation engine 112 doesnot calculate Restaurant X1 as a high quality recommendation as therecommendation engine 112 can not determine many similarities to othervenues based on effectiveness data in the data repository 118.Therefore, although Restaurant 6 has a low overall link strength 2114,the recommendation engine determines that a normalization factor shouldbe generated based on Restaurant 6 and Restaurant 3 in order to elevatethe overall link strength of these restaurants past the recommendationthreshold. In the example illustrated in FIG. 21C, the recommendationengine 112 determines a quality factor of 0.65 for Restaurant 3 as ithas multiple similarities to previously determined effectiverecommendations. Further, the recommendation engine 112 determines aquality factor of 0.55 for Restaurant 6 as it has fewer similarities topreviously determined effective recommendations stored in the datarepository 118. For Restaurant X1, the recommendation engine 112determined a quality factor of 0.15. At this point, assuming a userspecified or system 100 specified recommendation limit of two, therecommendation engine 112 must calculate a normalization factor based onthe quality factors such that two of the overall link strengths areelevated above the 0.25 recommendation threshold. Accordingly, exemplaryvalues of overall link strengths are illustrated in FIG. 21C based onnormalization based on the quality factors such that Restaurant 3 andRestaurant 6 are both above the recommendation threshold. Therefore,even with a low overall link strength, Restaurant 6 is still recommendedin this example as two recommendations were required to be provided toUser 2 and Restaurant X1 had an extremely low quality factor.

The recommendation engine 112 may also perform geometriccontextualization based on the diversity of the recommendations in therecommendation set. For example, assuming the recommendation engine 112generates six recommendations, only three of these recommendations maybe related whereas the other three may be diverse from each other andthe three related recommendations. For example, three of therecommendations may relate to restaurants all have the same genre andneighborhood whereas the other three recommendations have differentgenres and neighborhoods. The recommendation engine 112 may furthercompare price points and venue attire to determine similarities betweenthe recommended venues. Accordingly, content-based links, collaborativelinks and content-collaborative links between the recommended venues aredetermined by the recommendation engine 112 to determine overall linkscores therebetween thereby identifying which recommended venues aremost closely related and which recommended venues are diverse from eachother. The recommendation engine 112 may then determine a normalizationfactor to elevate recommendations that are similar to each other abovethe recommendation threshold as it is likely that multiple similarrecommendations are closer to the affinity of the user as opposed to avariety of potential outlying recommendations have little to norelationship therebetween.

An example of geometric contextualization using a normalization factorbased on the diversity of the recommendations in the recommendation setis illustrated in FIG. 21D with reference to FIG. 21A. In this example,as with the previous examples, it is assumed that the recommendationengine has generated Restaurants 3, 6, and Restaurant X1 in response tothe user query identified for FIG. 21A. In this example, assuming arecommendation threshold of 0.25 and an overall link strength value Gthat is less than 0.25 for Restaurant X1, none of the overall linkstrengths 2112 exceed the recommendation threshold. Accordingly, thesystem 100 determines that the recommendation engine 112 needs toperform geometric contextualization on the recommendation data. Whenperforming geometric contextualization, the recommendation engine 112determines that Restaurant 6 and Restaurant X1 are closely related basedon content-based links, collaborative links and content-collaborativelinks and that Restaurant 3 is quite diverse from both Restaurant 6 andRestaurant X1. For example, the recommendation engine 112 can determinefrom data stored in the data repository 118 that Restaurant 6 andRestaurant X1 have similar price points, similar neighborhoods, similarreview data and similar hours of operation. Accordingly, therecommendation engine 112 determines a diversity factor for Restaurant 6based on similarities between Restaurant 6 and Restaurant 3 andRestaurant X1. Further, the recommendation engine 112 repeats thisdiversity determination for both Restaurants 3 and X1 to determine theirdiversity factor with respect to the other restaurants in therecommendation set illustrated in FIG. 21D. Based on thesedeterminations, exemplary values of diversity factors are identified inFIG. 21D with respect to each recommended venue. According to thisexample, recommended venues having a lower diversity factor are venuesthat have more similarity to other venues win the recommendation setwhereas recommended venues having higher diversity factors are venuesthat are not that related to other venues in the recommendation set. Forexample, Restaurant 6 has the lowest diversity amongst the recommendedvenues and therefore has a diversity factor of 0.25 whereas Restaurant 3is the least related amongst the venues and has a diversity factor of0.75. Accordingly, exemplary values of overall link strengths areillustrated in FIG. 21D based on a normalization factor generated basedon the diversity factors such that Restaurant 6 and Restaurant X1 areboth above the recommendation threshold.

Geometric contextualization can also be performed with respect to adistance function defined by the user and/or the system 100 and storedas part of the user attribute data illustrated in FIG. 6. For example,each user may predefine his own individual distance function identifyinggeographic preferences with respect to any recommendations made on hisbehalf The system 100 may also further redefine this distance functionbased on the local geography and cultural geography of a given location.For example, the user may live in a city and not have a car such thatthe system 100 defines the local public transportation boundaries as adistance function for recommendations such that any recommendationserved to the user should be within those boundaries. The user may alsoidentify the city limits as their geographic distance function. Further,the system 100 can analyze information from different cities to identifywhich cities are, for example, walking cities and which cities allow forbroader transportation options. As such, for walking cities, the system100 will define a smaller geographic distance function where as fordriving cities, such as Los Angeles, the system 100 will define a largergeographic distance function. Accordingly, in selected embodiments, aradial distance function is defined for the user such that therecommendation engine 112 generates recommendations within theboundaries defined by the user and the system 100 even if theserecommendations do not have as high of an overall link strength as otherrecommended venues in neighborhoods outside the users radial distanceparameters. Further, for example, if the recommendation engine 112generated 50 recommendations but only 20 of those were within thedistance function defined with respect to that user, the recommendationengine 112 may focus only on the 20 geographically appropriaterecommendations and then serve a recommendation set based on those thathave overall link strengths above the recommendation threshold. In otherselected embodiments, the system 100 may in this instance serve apercentage of those recommendations have the highest overall linkstrengths that are within the number of recommendations in the radialdistance of the user.

The system 100 may also provide recommendations based upon a combinationof the above-noted geometric contextualization methods. For example, thesystem 100 may perform geometric contextualization based on the numberof potential recommendations, the individual quality of therecommendations, the diversity of the recommendations, the distancefunction and serve the user with recommendations based on the results ofall three geometric contextualization processes. The recommendationengine 112 may determine a subset of recommendations within thegenerated recommendation set that are determined to be higher quality,determine an adequate normalization factor based on the number ofrecommendations in the subset and then perform geometriccontextualization based on the normalization factor to generate therequisite amount of recommendations that are above the recommendationthreshold. Further, the recommendation engine 112 may determine a subsetof recommendations by determining which recommendations comply with thedistance function identified by the user and/or system 100, determine aranking of these recommendations in terms of quality with respect tooverall link strength and effectiveness data and provide a heavyweighting factor to this ranking based on quality factors, determinewhich of these recommendations have the lowest diversity factor andslightly adjust the ranking to ascend recommendations having lowerdiversity factors and lower recommendations having high diversityfactors, and then identify the number of potential recommendations toidentify a percentage of recommendations that should be served to theuser based on overall link strengths. In selected embodiments, therecommendation engine 112 can identify overlapping recommendations basedon the various methods and serve these recommendations to the user. Therecommendation engine 112 may also weigh the various methods ofgeometric contextualization based on their perceived effectiveness basedon the recommendation data set and provide recommendations to the userbased on overlap and weighting effects of the different processes.

Further, in selected embodiments, the normalization factor generated bythe recommendation engine 112 may be calculated based on a combinationof data relating to the number of potential recommendations available inthe filter state, the aggregate or individual quality of thoserecommendations, and the diversity of those recommendations.Accordingly, the recommendation engine 112 can calculate a normalizationfactor by using calculated data values based on these factors as inputs.The normalization factor is then applied as described above to elevatethe requisite amount of recommendations above the recommendationthreshold.

As discussed previously herein, in other selected embodiments or incombination with the above described geometric contextualizationmethods, the system 100 may require that a certain percentage ofrecommendations out of the recommendation set be served to the user foreach user query. This percentage can change based on factors such as thenumber of recommendations generated or the number of recommendationsgenerated that have overall link strengths above the predeterminedthreshold. Accordingly, upon determining a set of recommendations basedon overall link strength as previously described herein, therecommendation engine 112 may perform geometric contextualization toredefine a ranking of the recommendations based on at least the numberof the recommendations, the quality of the recommendations, thediversity of the recommendations, and the distance function in order toprovide an enhanced set of recommendations to the user. Upon ranking therecommendations in the recommendation set based on geometriccontextualization, the most highly ranked recommendations are selectedin descending order until the percentage of required recommendations ismet. This set of recommendations is then server by the recommendationengine 112 to the user via the user interface.

In selected embodiments, and to lower processing requirements on thesystem, geometric contextualization can be performed only when apredetermined number of recommendations generated by the recommendationengine 112 based on overall link strength do not have overall linkstrengths above the recommendation threshold. Geometriccontextualization then elevates the required amount of recommendationsabove the recommendation threshold such that the required amount ofrecommendations can be provided to the user via the user interface.However, in other selected embodiments, the system 100 may performgeometric contextualization for every search query performed by the usersuch that recommendations generated by the recommendation engine 112 andprovided to the user based on overall link strength are further enhancedand reordered based on at least the number of potential recommendations,the quality of the recommendations, the diversity of the recommendationsand the distance function of the particular user or users. Regardless ofhow geometric contextualization is performed, the system 100 can servethe recommendation data or datum to the user via the user interface withthe caveat that the system had to perform some additional processingbased on the filter state to obtain the specified number ofrecommendations. Therefore, the user can be warned that although theyhave been provided the requisite number of recommendations that theserecommendations did not perfectly match the filter state and shouldtherefore be strongly considered. The system 100 could also provide theuser with information based on the type of geometric contextualizationperformed with respect to the filter state. For example, if geometriccontextualization is performed based on the number of potentialrecommendations and known user characteristics, the user may be informedthat a certain venue was selected outside the filter state based onprevious user characteristic venue price points. Alternatively, thesystem 100 could provide the user with a combination of recommendationsbased on this geometric contextualization method such that the system100 informs the user that one recommendation is based on elevatedoverall link strength and the other recommendation is based onpreviously known user characteristics.

A description of the geometric contextualization according to selectedembodiments is illustrated in FIG. 22. First, the recommendation engine112 calculates the recommendation set at S2200 based on the filter stateprovided by the user at the time of the query. Accordingly, therecommendation engine 112 may determine a large set of recommendationsbased on overall link strength with respect to an affinity of a venueprovided by the user but the number of recommendations in thisrecommendation set will be lowered based upon the user filter state.Therefore, the recommendation engine 112 must determine at S2202 whethergeometric contextualization is required based upon the recommendationset. If there is a large number of recommendations in the recommendationset that exceed the recommendation threshold, the recommendation engine112 may determine that geometric contextualization is not requiredthereby lowering the processing load on the system and providing quickerresults to the user. At this point, the recommendation engine 112proceeds to S2210 to provide the one or more recommendations to theuser. However, if there a requisite amount of recommendations thatexceed the predetermined threshold has not been generated, therecommendation engine 112 determines at S2202 that geometriccontextualization is required. Further, the system may find a number ofrecommendations that exceed the recommendation threshold but if thisnumber is lower than a user or system 100 specified number ofrecommendations required to be presented to the user, the recommendationengine 112 will proceed with geometric contextualization to obtain therequisite amount of recommendations. Further, in other selectedembodiments, the recommendation engine 112 may always perform geometriccontextualization on the recommendation set to further redefine aranking of recommendations in the recommendation set based on theabove-noted input factors such as quantity of recommendations, qualityof recommendations, diversity of recommendations and user distancefactors.

Upon determining that geometric contextualization is required, therecommendation engine 112 determines the normalization factor based onthe information contained in the recommendation set itself as previouslydescribed. Therefore, the recommendation engine 112 determines whichmethod or combination of methods for generating a normalization factorwill be the most effective and proceeds to generate the normalizationfactor based on these methods at S2204. The recommendation engine 112then normalizes the overall link strength values identified from thesearch results to obtain at least one recommendation with an overalllink strength value above the recommendation threshold.

Once the recommendations have been normalized, the recommendation engine112 analyzes at S2208 the normalized recommendation values to ensurethat the recommendation set now contains enough recommendations valuesthat exceed the recommendation threshold. If the number ofrecommendation values exceeding the recommendation threshold is stillbelow the number of recommendations required by the user or system 100for search results, the recommendation engine 112 repeats steps S2204 toS2208 to determine another appropriate normalization factor andre-normalize the recommendation set. This process is repeated until therequisite number of recommendation values exceeding the recommendationthreshold is obtained. Once the recommendation engine 112 determines atS2208 that there is an adequate number of recommendations greater thanthe recommendation threshold, the recommendation engine 112 serves theone or more recommendations to the user via the user interface at S2210.The user can then perform additional searches and/or set a furtherfilter state to further refine the search for additionalrecommendations.

As calculating what recommendations the recommendation engine 112 willgenerate for each possible filter state provided by a user ahead of timeis extremely difficult and time consuming, the system 100 in selectedembodiments performs the geometric contextualization in “real-time” toprovide enhanced recommendation accuracy at the time of a search.Accordingly, via geometric contextualization processing, the system 100can ensure that the recommendation engine 112 will always provide atleast one recommendation to the user regardless of the filter state.This bolsters user confidence in the system and decreases the likelihoodthat users migrate to other systems.

Further, once geometric contextualization has been performed and therecommendation engine 112 has determined a recommendation set having anadequate number of recommendations, the system 100 may then performerror correction and data verification to further ensure and/orstrengthen the accuracy of the recommended venues which may in turn beused to identify reviewers who have submitted review data with respectto the recommended venues and venues in other locations requested by theuser.

Interconnectivity Augmentation

As previously described, the system 100 can present a user withrecommendations based on user input and neural connections createdbetween a variety of nodes via content-based relationships,collaborative relationships and content-collaborative relationships.Therefore, a user in Boston can get recommendations for other venues inBoston based on overall link strengths and post-recommendationprocessing performed by the recommendation engine 112. However, an issuearises when a user in one geographical area wants to get recommendationsfor venues in a geographically distant or diverse geographic location inwhich the system 100 does not contain much information about userinterests. For example, a user in Boston may have an upcoming trip toNew York and may query the system 100 for recommendations on places toeat in New York. This request may be difficult if there is aninformation deficit within the system 100 such that it contains largeamounts of information as to which venues the user likes in Boston butnot much, if any, information on the likes or dislikes of a user withrespect to New York. In other words, neural network connections betweenthe extensive neural network topology of the user in Boston and thelimited neural network topology of the user in New York are not welldefined. Therefore, the system 100 must use information known about theuser in Boston to extrapolate information the recommendation engine 112can use to generate a set of recommended venues in New York.

In one embodiment, the system 100 can use information relating to thegeometric locale of the user performing the search by taking advantageof a predetermined amount of interconnectivity of venues in the neuralnetwork developed at that geometric locale. For example, FIGS. 7 and 8represent neural network interconnectivity based on connections formedvia content-based and collaborative interrelationships defined based onuser data, review data and venue data as previously described. Thisinformation is based on Restaurants 1-12 that are located in Boston,Mass. Therefore, the system 100 already has at its disposal a variety ofinterconnectivity information with respect to the user and venues in thestate of Massachusetts. The recommendation engine 112 can then, at thetime of generating a recommendation, use review data relating to thelocal venues as well as the venues in New York to determine connectionsto venues in New York that are most closely related to venues in Boston.

First, the recommendation engine 112 receives which locale the userwould like to obtain recommendations for and polls reviewer informationto identify reviewers who have reviewed venues in the locale the user issearching for and the locale the user is located in when performing thesearch. This reviewer information is obtained from a variety of sourcesas described above with respect to web crawling and the identificationof venue reviews. Once the recommendation engine 112 has determined theplurality of reviewers having provided reviews for both locales, therecommendation engine 112 processes the geometrically interconnectedreview data to form collaborative interrelationships values such thatthe system 100 can augment the neural network based on the collaborativenodal link values between the two locales. Accordingly, therecommendation engine 112 determines positive or negative affinityconnections between a variety of venues within multiple locales based onthe review data linking the locales via collaboratively formedinterrelationships. At this point, the data repository 118 containsamplified inter-connections between venues in both locales from whichthe recommendation engine 112 can draw upon to make recommendations forthe user.

To determine which venues from the geographically distance locale torecommend to the user, the recommendation engine 112 looks for strongoverall link strengths between venues in the geographically distancelocale and the venue(s) the user expressed an affinity for as part ofhis search query or venues the user is known to like. The venues in thegeographically distant local having the strongest overall link strengthsto these venues are then generated by the recommendation engine 112 andserved to the user via the user interface. Therefore, the system 100 canprovide a user with the ability to identify venues of interest inforeign locales by using review data between locales in which the system100 contains a highly developed neural network topology with respect touser interests and a foreign domain in which the system 100 does nothave much information about user interests by augmenting the neuralnetwork interconnectivity therebetween via correlative review data.FIGS. 23 and 24 provide an illustrative example of the initialprocessing of interconnectivity augmentation of determininginterconnection data between two geographically diverse locals inresponse for a user query for a venue in New York based on an affinityfor a venue in Boston. For the purposes of this example, it is assumedthat the user lives in Boston and would like to determine a venue ofinterest for his upcoming trip to New York. Assuming the system 100contains a well-developed neural network topology for users interests inBoston but contains little information on users interests in New York,the system 100 must augment the existing neural network establishedbased on interrelationships developed in Boston to include additionallinks to venues located in New York. Accordingly, in selectedembodiments, all of the review data in which reviewers have providedreviews for both Boston and New York venues is identified and is used todetermine collaborative relationships values between the venues in bothcities. This information can then be used to update interconnectivitybetween the neural network topology in both Boston and New York therebyallowing the system to follow links from Boston to New York to recommendvenues to the user.

To increase efficiency and decrease processing demands, in selectedembodiments the system 100 may only perform interconnectivityaugmentation with respect to venues that are closely related to thevenue in which the user has expressed an affinity for in his search asdetermined based on at least the overall link strength or other methodsdescribed above. For example, in this embodiment if the user expressesan affinity for an American restaurant having casual attire and a lowprice point, the recommendation engine 112 will determine a set ofvenues having a strong overall link strength with respect to thisrestaurant within Boston and then perform interconnectivity augmentationto determine which of these venues have review data in which thereviewer also provided data for venues in New York. If there is notenough review data to determine ample interconnectivity informationbetween the generated venues in both locales, the recommendation engine112 will identify a plurality of other venues having a strong overalllink strength with the previous set of generated venues and the system100 will again determine if there is enough review data between bothlocales such that nodal links between both Boston and New York can beupdated in a way that allows the recommendation engine 112 to recommendvenues in New York with a high level of confidence. This process isrepeated until the system 100 determines that a predetermined number ofvenues in which review data is available for both locales has beenreached. FIG. 23 provides an illustrative example of a plurality ofvenues identified by the system 100 in which Reviewers 1-5 have providedreview data for both Boston and New York. For illustrative purposes andthe ease of explanation, FIG. provides three venues in both New York andBoston but it is noted that this is only a non-limiting example asadditional restaurants would likely be included when performinginterconnectivity augmentation. As seen in FIG. 23, Reviewer 1 is anavid reviewer and has provided a plurality of reviews in both Boston andNew York and Reviewer 3 is less active and has only provided review datafor one restaurant in New York and Boston. For the purposes of thisexample, it is assumed that Restaurant D is the actual venue the userexpressed an affinity for when performing a search for restaurants inNew York, Restaurant E is a restaurant having a strong overall linkstrength to Restaurant D, and Restaurant F is a restaurant having a lowoverall link strength to Restaurant D. Assuming that an adequate numberof venues having reviews in both Boston and New York have beenidentified by the system 100, collaborative based link strengths aredetermined based on the review data.

FIG. 24 illustrates the collaborative based links strengths betweenRestaurants A-F based on the reviewer ratings illustrated in FIG. 23.Accordingly, Restaurant A and Restaurant F have a strong collaborativeinterrelationship nodal link value of +1.5 based on strong reviews byReviewer 1 whereas Restaurant D and Restaurant F have a very lowcollaborative nodal link value of −1.5 based on opposite affinitiesexpressed by both Reviewer 1 and Reviewer 2. These collaborative venuelink values represent interconnections between Restaurants A-C of NewYork and Restaurants D-F of Boston. Therefore, as previously discussed,through the process of interconnectivity augmentation, the system 100traces sparse cross connections between venues of different locals(Boston and Yew York) to create “spider webs” of informationtherebetween. The recommendation engine 112 can then navigate links ofthese spider web based on overall link strengths to determinerecommended venues in New York based on the neural network topologyinterconnections between Boston and New York.

FIG. 25 presents a connectivity diagram illustrating the spider webgenerated by the system 100 based upon a user query for a restaurant inNew York and the review data obtained for venues in both New York andBoston. Solid connections between the venues represents a positivecollaborative link between the venues whereas dotted connectionrepresent a negative collaborative anti-link between the venues.Further, the thicker the line illustrated in FIG. 24, the stronger thevalue (either negative or positive) for that particular nodal link. Forexample, the nodal link 2500 between A and F is a solid line withrelative thickness based on the overall collaborative link strengthvalue of +0.75. Further, the nodal anti-link 2502 between D and F is hasa relative thickness and is dashed to represent a negative overallcollaborative link strength of −1.5. Based upon these links created bythe system 100, the recommendation engine 112 can determine a variety ofrecommendations for the user for New York based on the overallcollaborative interconnectivity link strengths between the restaurantsin Boston and New York.

For example, as it is assumed that Restaurant E is the actual restaurantin Boston the user expressed an affinity for in his search query, therecommendation engine 112 may generate a recommendation containingRestaurant A in New York as Restaurant A is directly linked toRestaurant E via nodal link 2500 and has a positive collaborative nodallink value. The recommendation engine 112 may also recommend RestaurantB as it is linked to Restaurant E via Restaurant A (nodal link 2504) andhas a strong nodal link strength with Restaurant A. As Restaurant C doesnot have any collaborative connections to Restaurant E or Restaurant A,the recommendation engine 112 would likely ignore this node whenpresenting New York venue recommendations to the user.

Assuming the user expressed an affinity for Restaurant F in his searchquery for recommended venues in New York and the network topology wasdefined by the system 100 based on reviewer data as illustrated in FIG.23, the recommendation engine 112 may still recommend Restaurant A overRestaurant B even though both Restaurant A and Restaurant B are linkedto Restaurant F as Restaurant A has a stronger collaborative nodal linkstrength with Restaurant F via link 2500. However, Restaurant B couldalso be served to the user as an alternative choice. However, if therestaurant for which the user has expressed an affinity for in thesearch query is not included in the network topology data because therewas not enough reviewer information relating to that restaurant, thesystem 100 will determine the venue having the strongest overall link tothe restaurant provided in the search query but which also has amplereview data with respect to venues in the foreign local in which theuser is seeking recommendations. The recommendation engine 112 can thenavigate the neural network amplified via interconnectivity augmentationto find a recommendation in New York based on the venue in Boston havingthe strongest overall link strength value to the venue the userexpressed an affinity for in his search query.

Accordingly, the interconnectivity augmentation process determinesvenues having strong overall link strengths with venues the userexpresses an affinity for when performing a search query and thendetermines a plurality of reviewer data with respect to these venues andthe location in which the user is requesting recommended venues. Anetwork topology based upon the collaborative values between venues withrespect to the review data is generated and it is determined whetherthere enough information from which the system 100 can make arecommendation to the user. If there is not enough information, thesystem 100 generates additional local venues with links to therestaurant provided in the search query and the network topology isupdated based on review data with respect to the venues and the venuesin the foreign local. Once there is a predetermined amount ofcollaborative link strength data between the plurality of venues bothwithin the locale of the user and the foreign local, the recommendationengine 112 determines recommended venues in the foreign local byfollowing the strongest overall nodal links in the network topologywhile starting at the local node expressed in the search query or thelocal node having the strongest overall link to the venue identified inthe user search query. The recommended venues are then served to theuser via the user interface.

If the user provides additional filters with the search query inaddition to the affinity for a particular venue, the system 100 willtake this into account when creating the network topology by harvestingdata on the local and foreign venues and identifying which datacorresponds to the data within the filter. For example, if the Bostonuser is a New England Patriots fan and is looking to watch the Mondaynight game in New York while avoiding heckling from New York Giants fanswho mistakenly believe Eli Manning is better than Tom Brady, the usermay indicate that he would like a restaurant identified as Patriotsfriendly. Accordingly, based on the example illustrated in FIG. 25 inwhich the user expressed an affinity for Restaurant E in Boston as partof his search query, if Restaurant A is a Giants friendly venue andRestaurant B is a Patriots friendly venue, the recommendation engine 112will recommend Restaurant B over Restaurant A even though Restaurant Ahas a stronger overall collaborative link value to Restaurant E based onthe reviewer data therebetween. Further, the user may have kids and willtherefore want a venue that is friendly to children. Therefore, venueattributes and user characteristic attributes can also be taking intoaccount by the system 100 when performing interconnectivity augmentationto determine recommendations in locations where the system 100 does nothave a lot of information about what the user likes in that particularlocation.

In other selected embodiments, the system 100 may performinterconnectivity augmentation to recommend venues in a foreign local inwhich the system 100 has very little information about user interests bytaking into account user specific information known to the system 100 inother locals As with the examples discussed above, the system 100 maynot have much information about what the user likes in New York but mayhave ample information about what user likes in Boston. Accordingly,user attributes, review data from the user, previous recommendationsknown to have been effective and the interrelationships formed based oncontent and reviewer data from the user local can all be used toextrapolate venue “clones” in a foreign local that are similar to oridentical to venues known by the system 100 to be well received by theuser. These nodal doppelgangers can then be incorporated into the neuralnetwork topology previously defined as discussed above based on contentand collaborative interrelationships within the local area of the usersuch that the system 100 can follow the nodal links to determine venueclones in a foreign locale that may be of interest to the user. Further,the list of nodal doppelgangers will inherently be cross-connected witha plurality of other venues within the foreign locale such thatadditional recommendations can be made to the user. Accordingly,alternatively or in addition to review data between two locales,interconnectivity augmentation can also be performed based solely onuser interest information from other locales.

FIG. 26A is a chart illustrating an exemplary sample set of venueswithin New York, N.Y. that are stored within the data repository 118. Aswith FIG. 4, each Restaurant A-E has its own price, genre, hours ofoperation, attire and neighborhood. Accordingly, as described previouslywith respect to FIG. 4-12 and FIG. 23, the system 100 containsinformation, determined via web crawling and web harvesting, aboutvenues in Boston and nodal interrelationships therebetween and knowsinformation about venues in New York but does not have information onnodal links between the venues in Boston and the venues in New York.Therefore, interconnectivity augmentation can be performed utilizingnodal cloning in order to determine intercity nodal relationshipsbetween Boston and New York. Assuming the user is located in Boston andhas performed a search query for venues in New York, the system 100 mustdetermine which venues the user is typically interested in Boston. Thesystem 100 can receive as part of the search query for venues in NewYork, restaurants in Boston that the user likes in which he wishes tofind similar restaurants in New York. The system 100 can also determinewhich venues the user likes based on previous recommendation data thathas been determined to be effective via financial transactions of theuser, positive review data, GPS data or other data as describedpreviously herein. The system 100 then compiles this list of interestsof the user within Boston and compares each venue or piece of interestto known venues in New York to determine nodal doppelgangers within NewYork that have many of the same features of the venues identified withinBoston. FIG. 26B is a chart illustrating the results of processing todetermine a congruency factor representing a similarity level betweenthe venues of interest from Boston and venues in New York. In FIG. 26B,it is assumed for the purposes of this example that both Restaurant 6and Restaurant 10 were included as part of the search query by the userfor restaurants in New York and/or were determined to be venues thatwere previously recommended and were effective with respect to the user.Accordingly, the system 100 compares each attribute of Restaurant 6 andRestaurant 10 to each attribute of a plurality of identified venueswithin New York in an attempt to determine one or more venue cloneshaving a high congruence factor with Restaurant 6 and Restaurant 10 ofBoston.

Based on these comparisons, Restaurant C of New York has the highestcongruency factor with respect to Restaurant 6 as the price is the sameas Restaurant 6, the genre is the same as Restaurant 6 and the attire isthe same as Restaurant 6. Conversely, Restaurant D has the lowestcongruency factor with respect to Restaurant 6 as the price point isextremely high, the genre is different and the attire is different. Withrespect to Restaurant 10 of Boston, Restaurant D has the highestcongruency factor as the price point is similar, the genre is the sameand the attire requirements are the same. Accordingly, by determiningthe congruency factors, the system 100 can identify venues in a foreignlocal that have the features similar to user-provided venue filtersand/or venues which have been determined to be effective for the user inthe past.

The congruency factor scores are exemplary and will change based onvarious venue attributes as well as different weights assigned tovarious venue attributes. For example, genre may be weighted the highestas a user searching for restaurants in New York who provides Restaurant6 of Boston as part of the search query will likely identify with NewYork restaurants that have the same type of food. Further, price may beweighted less than genre but more than attire. These weights can be setautomatically by the system 100 or manually by the user performing thesearch. The congruency score for Restaurant 6 of Boston in comparison toRestaurant C of New York is not 1.0 because it is assumed that there maybe other factors taken into consideration in determining how similarRestaurant C is to being a clone of Restaurant 6. These factors include,but are not limited to, hours of operation, review data, usercharacteristic data and previously defined nodal link interrelationshipinformation between Restaurant 6 and other restaurants in Boston or NewYork. The user may also indicate likes and dislikes which will affectthe weighting of the various venue attributes. For example, if the userdetests dressing up when going out to dinner, the system 100 may applyan extremely high weighting factor to the attire attribute therebycausing restaurants requiring formal attire to have extremely lowcongruency factors even though they are similar to the identified localrestaurant in many other respects. Once the system has determined theplurality of congruency scores with respect to the restaurants ofinterest identified based on the venues provided in the search query orthose known by the system 100 to be effective, the system 100 updatesthe neural network topology to form nodal links between the identifiedvenues of interest and the venues identified in the foreign local. Inselected embodiments, the system 100 may link all nodes between thelocales regardless of the congruency factor or may link only those nodeshaving a strong congruency factor therebetween. Accordingly, the system100 or user may assign a predetermined threshold congruency factor formagnifying nodal interconnectivity between various locales.

FIG. 27 represents nodal interconnection magnification between Bostonand New York based on the plurality of congruency factors determinedwith respect to Restaurant 6 and Restaurant 10 of Boston, andRestaurants A-E of New York. For the purposes of this example, FIG. 27only illustrates nodal inter-locale augmentation for nodes having acongruency score of at least 0.60 or better. As illustrated in FIG. 27,a nodal link 2700 is formed between Restaurant 6 of Boston andRestaurant C of New York as the congruency factor therebetween asdetermined by interconnectivity augmentation processing is 0.85.Further, nodal links 2702 and 2704 are formed between Restaurant 10 ofBoston and Restaurants A and D of New York as Restaurant 10 andRestaurant A have a congruency factor of 0.65 and Restaurant 10 andRestaurant D have a congruency factor of 0.75. The difference inthickness of the nodal link represents the strength of the congruencyfactor such that the thicker the nodal link the more similar the venuesare to each other. Accordingly, nodal link 2504 based on a congruencyfactor of 0.75 is thicker than nodal link 2502 having a congruencyfactor of 0.65. Further, nodal link 2500 based on a congruency factor of0.85 is thicker than both nodal link 2502 and nodal link 2504.Restaurants E, V, W, X, Y and Z are Restaurants in New York that havestrong overall link strengths to Restaurants C, D and A as illustratedbased on content-based interrelationships, collaborativeinterrelationships, content-collaborative interrelationships and tieredrelationships as described previously herein. Based on the amplifiedneural network formed between Boston and New York formed as a result ofthe interconnectivity augmentation performed in response to a searchquery for a venue in New York based on an affinity for Restaurant 6and/or Restaurant 10 of Boston, the recommendation engine 112 cantraverse the links in the updated neural network topology to providerecommendations to the user for venues in New York. For example, asillustrated in FIG. 27, for a user expressing an affinity for Restaurant6, the user will be served with a recommendation for Restaurant C in NewYork. Similarly, for a user expressing an affinity for Restaurant 10,the user will be served with Restaurant D and Restaurant A with anindication the Restaurant D was the closest venue based on the user'ssearch query. Further, interconnections within the neural network of NewYork can further be used to provide larger recommendation sets. Forexample, for a user expressing an affinity for Restaurant 6 in a searchquery, Restaurant E may be recommended next as having a strong overalllink strength to Restaurant C with Restaurant V and W being recommendednext as alternative venues based on their overall link strength withrespect to Restaurant C.

As described previously, if the user provides additional filters withthe search query in addition to the affinity for a particular venue, thesystem 100 will take this into account when creating the networktopology by harvesting data on the local and foreign venues andidentifying which data corresponds to the data within the filter. Forexample, if the user performs a search query by expressing an affinityfor Restaurant 6 but also provides a filter that requires a medium pricepoint for recommended venues, the recommendation engine 112 mayrecommend Restaurant E over Restaurant C as the recommendation engine112 is able to traverse the nodal link 2700 to determine the RestaurantC is a good match but further determines based on venues having strongoverall link strengths with respect to Restaurant C that Restaurant E isa better choice because it has a medium price point as compared toRestaurant C's low price point. Therefore, venue attributes and usercharacteristic attributes can also be taking into account by the system100 when performing interconnectivity augmentation to determinerecommendations in locations where the system 100 does not have a lot ofinformation about what the user likes in that particular location. Itshould be noted that the system 100 may also contain large amounts ofinformation with respect to user interests in locales other than the onein which the user is located that can also be used to performinterconnectivity augmentation based on congruency factors via adetermination of nodal doppelgangers. In other words, when a user fromBoston is looking for venues of interest in New York, the system 100 mayalso perform interconnectivity augmentation in the above-noted manner byusing recommendations known to be effective in other areas with respectto the user, such as Washington D.C., to further enhance the variety ofrecommendations provided to the user. Further, congruency factorsdetermined between different locales can be weighted differently basedon user time spent in the locales, the number of effectiverecommendations or the like and then compared to determine a moreaccurate recommendation set for the user. Additionally, the system 100may encounter difficulties performing interconnectivity augmentationbetween Boston and New York if venue information from New York is notreadily available. In such a situation, if the system 100 alreadycontains strong links to another city, such as strong links betweenBoston and Washington, D.C., and strong links from Washington, D.C. toNew York, the system 100 may determine recommendations by navigating thenodal network topology from Boston to New York via nodal links providedin Washington D.C.

Although the links may be uni-directional or bi-directional aspreviously described herein, the nodal links determined viainterconnectivity augmentation as illustrated in FIGS. 25 and 27 arebi-directional to further enhance information available to the system100 in the event that similar future searches are performed by the sameuser or other users with similar interests either in Boston or New York.

As note above, the system may perform interconnectivity augmentationbased on both review data between different locations and via adetermination of nodal doppelgangers in different locales. Accordingly,the system 100 may update the neural network topology of nodes betweendifferent locales based on review data and then may further update thisnetwork topology based on nodal link determinations identified viacongruency factors. As such, interconnectivity augmentation thereforeprovides the system 100 with the ability to extrapolate informationabout foreign systems to generate an updated neural network topologyhaving connections between a locale in which the system has ampleinformation about what the user likes and a local in which the system100 has very little information about what the user likes. This providesenhanced functionality to the user in that the system 100 acts as atravel companion to provide venues of interest to a user when a user istraveling to various locations. This increases the likelihood that theuser will enjoy his experience when traveling and will further enhancethe network topology thereby increasing the accuracy of futurerecommendations to the user.

Further, error correction and data verification can be performed toensure and/or strengthen the accuracy of the recommended venues whichmay in turn be used to identify reviewers who have submitted review datawith respect to the recommended venues and venues in other locationsrequested by the user.

Merchant Interface

The venues are operated by third parties which may comprise merchantssuch as restaurant owners, airlines, or hotel operators. The system 100may be configured to provide merchants a visualization of users'behavior. For instance, merchants may be provided access to ant traildata patterns, including in real time. Merchants can “interact” withthese patterns and request the system 100 to inject disruptive contentsuch as promotional offers related to a user's present location andexpressed preferences.

Merchants may also be provided anonymized profiles of the likes anddislikes of their customers (i.e. users who patronize theirestablishment). This can include reviews provided by reviewers and userswho provide feedback (who also constitute reviewers). Additionally, itis anticipated that merchants will likely wish to providepersonalization services to their customers to ensure customer retentionwhile increasing revenue. For example, merchants selling products eitheronline or in the brick-and-mortar world may want to identifyrecommendations for their customers based on at least previous purchasesby the customer, customer attribute data, review data, data about theproduct itself and the accompanying neural network topology generatedbased such information. However, it is unlikely that merchants will havethis functionality to provide users, much less the ability to providethese types of services on the scale and accuracy of the system 100.Accordingly, the system 100 provides an application programminginterface (API) operated by the server 102 for allowing merchants tosupply data to the system 100 which can be used by the recommendationengine 112 to determine recommendation or similarity data. The system100 then sends this data back to the merchant.

FIG. 28 illustrates an exemplary interaction between the system 100 anda plurality of merchants/third party vendors 2800, 2802 and 2804 via anAPI 2801. FIG. 28 includes the server-based recommendation generationsystem 100 hosted on the server 102 as illustrated in FIG. 1 andtherefore like designations are repeated. Further, the merchantinterface 116 is illustrated as including the above-noted API 2801. Asillustrated in FIG. 28, the server 102 is connected to a plurality ofmerchants 2800, 2802 and 2804 via the network 120. Each merchant canprovide the server 102 with a plurality of requests which are receivedby the API 2801 via the network 120. The requests can includeinformation such as identification information of the merchant, the typeof request, the type of information included in the request, the datawhich the system 100 will process, a request for a quote for processingthe request, and temporal information with respect to the requestitself. In response to receiving requests from the merchants, therecommendation engine 112 processes the requests and generates resultswhich are output to the merchants via the API 2801 and network 120.

In selected embodiments, the API 2801 includes a set of programminginstructions and standards for accessing the system 100 such thatmerchants can appropriately format their requests in a manner understoodby the system 100 and so the API 2801 can provide responses to themerchants in a manner manageable by their systems. In other words, theAPI 2801 provides programming such that the server 102 and remoteapplications operated by the merchants 2800, 2802 and 2804 cancommunicate with each other through a series of calls. For example, webservices having a collection of technological standards and protocols,such as extensible markup language (XML) may be provided therebyallowing various parties from different systems to communicate. The API2801 may be included as part of a software development kit (SDK) alongwith programming tools thereby providing the merchants 2800, 2802 and2804 with instructions on how to best interact with the system 100.Additional technological standards, protocols and programming languagesmay be included such as simple object access protocol (SOAP) forencoding XML messages so that they can be understood by operatingsystems over any type of network protocol, and universal description,discovery and integration (UDDI) for allowing the merchants 2800, 2802and 2804 to list themselves. Mashups may also be implemented within thesystem 100 thereby providing functionality from the API 2801 of thesystem 100 in conjunction with other web applications.

For the ease of explanation, FIG. 28 illustrates three merchants 2800,2802 and 2804 providing different requests to the server 102 but one ofordinary skill in the art would clearly recognize that more than threemerchants can connect to the server 102. In FIG. 28, it is assumed thatthe merchant 2800 has identified a cluster of items of which a customerof the merchant 2800 has expressed a preference as determined bypurchase data, shopping habits, and other methods as would be understoodby one of ordinary skill in the art. For the purposes of this example,it is assumed that the customer of the merchant 2800 has purchased threebottles of wine (or has three bottles of wine in their shopping cart)and is looking for one more bottle of wine. The merchant 2800 may wantto provide the customer with a recommendation of additional wines forpurchase and therefore interacts with the server 102 via the request2806 and API 2801. The request 2806 defined by the API 2801infrastructure includes a request for the recommendation engine 112 todetermine one or more recommended items that the user may like based onthe cluster of items included in the request 2806. The request 2806 mayinclude identification information of the merchant 2800, informationspecifying the type of request such as a request for recommendationdata, a categorical description of the data, and the data of which thesystem 100 will determine recommendations. For example, the request 2806may include the name and address (virtual and real-world) of merchant2800, a request for recommendations based on the data set provided bythe merchant 2800, information specifying that the data set relates tobeverages or more specifically alcoholic beverages such as wines, andthe list of wines for which the merchant 2800 would like the system 100to process. This request 2806 may be served to the system 100 at thetime of a purchase by the customer or at a later time when the merchant2800 is attempting to determine advertisement information based onprevious customer activity.

Once the request 2806 is received by the API 2801 via the merchantinterface 116 and network 120, the system 100 determines the type ofrequest and the type of information included in the request. Forexample, the system 100 parses the system call from the merchant 2800 todetermine that the recommendation engine 112 must generaterecommendations as described above based on the list of wines providedin the request 2806. If the type or genre of information is not includedin the request, the system 100 may generate an error message to themerchant 2800 or may attempt to determine the type of information. Forexample, the recommendation engine 112 may attempt to determine whetherthe items within the request 2806 relate to cars, food, video games, oras in this instance, wine based on keywords identified in the request2806 itself.

Once the recommendation engine 112 has determined the type of requestand what type of information is included in the request, therecommendation engine 112 identifies whether the data repository 128includes the appropriate neural network topology generated by the matrixbuilder 126 in which to process the request 2806. Specifically, inselected embodiments the system 100 may generate network topologies inall types of fields and for all types of products in addition to thevenues discussed above. Therefore, the data repository 118 may alreadycontain a neural network topology for the cluster of items relating towine contained in the request 2806. In other embodiments, the system 100may dynamically generate a new network topology or update a previouslyexisting network topology as previously described herein (viaharvesting, creating nodal links, error correction and dataverification, interconnectivity augmentation, etc) based on the type ofitems identified in the request 2806.

Once the system 100 has determined that a suitable neural networktopology exists for which to process the request 2806, therecommendation engine 112 generates a recommendation set for themerchant 2800 of items that users may like based on the cluster of itemsprovided in the request 2806. Specifically, the recommendation engine112 generates recommendations based on the methodologies describedpreviously herein such as identifying overall link strength rankings,performing error correction and data verification with respect to sourcesites, performing geometric contextualization with respect to thegenerated recommendation set and performing resonance checking of eachrecommendation in the recommendation set. Once the final recommendationset is generated by the recommendation engine 112, the server 102provides the recommendation set including a plurality of recommendedwines to the merchant 2800 in a response 2808 via the merchant interface116, API 2801 and network 120.

FIG. 28 illustrates another example of a request 2810 from merchant 2802including at least the merchant 2802 identification information, arequest that the system 100 provide information as to what items may besimilar to the items provided in the request 2810, an identification ofthe items as beverages and the list of one or more wines. For example,assuming that the data items included in the request 2810 again includethree bottles of wine, the merchant 2802 requests information as to whatother drinks may be similar to the bottles of wine such as other wines,champagne or liquor, that the user may like based on a similarity to thewine bottles included in the request 2810. In this instance, therecommendation engine 112 identifies the type or genre of informationincluded in the request 2810 and determines whether the appropriateneural network topology exists for similar items. Specifically, therecommendation engine 112 determines a plurality of items that may besimilar to the items included in the request 2810 and determines whetheran adequate neural network exists for each item.

The system 100 may determine similarity to an item in a variety of ways.For example, in selected embodiments, the system 100 determines thatbottles of wine fall into a drink category and therefore that onlydrinks should be contemplated by the system 100 when attempting todetermine similarities. This can be accomplished by performing keywordsearches with respect to the items included in the requests and/or byreferring to a previously defined database updated based on user andvendor requests. Next, additional subcategories are determined until thesystem 100 identifies a plurality of categories deemed to be mostsimilar to the items included in the request. For example, the system100 may further identify the wine is a type of alcohol and therefore thesystem 100 should only search for other alcohols such as beers andliquors. Accordingly, the system 100 can generate a similarity score ofvarious beverages based on nodal link strengths between beverages havingsimilar attributes within the neural network. In other selectedembodiments, the merchant 2802 may also provide in the request 2810similar categories to search based on the items provided by the merchant2802.

Once a list of items deemed to be similar to those listed in the request2810 has been determined, the recommendation engine 112 generates arecommendation set as previously described herein and provides therecommendation set to the merchant 2802 in a response 2812.Specifically, overall link strengths are calculated by therecommendation engine 112 via collaborative-interrelationships databased on reviewer data between wines identified in the request 2810 anditems deemed to be similar (such as liquors and beers), content-basedinterrelationships based on similar attribute data with respect to userswho both drink the wines identified in the request 2810 and liquors andbeers and wine attribute data such as the type of wine, the alcoholcontent, the location in which the wine is created, andcontent-collaborative interrelationships. Further, interconnectivityaugmentation may be performed to bolster the neural network connectivityas well as geometric contextualization and error correction and dataverification to enhance recommendation accuracy.

As described previously above, the requests coming from the merchantsmay also include filters with respect to the items included in therequests. For example, Merchant 2800 may request that the system 100only return recommendations for wines that were made in or before acertain year. Merchant 2802 may request that recommendations for similaritems be restricted to different types of beer rather than alsoincluding liquor as a subset. Accordingly, the recommendation engine 112is able to provide merchants not only with recommendations for the sameitem or similar items included in requests but is also able to fine tunerecommendations specifically tailored towards merchant requirements.Further, requests may include user attribute information from themerchant thereby providing the system 100 with additional informationfrom which to generate a recommendation.

The API 2801 further provides the functionality for merchants tocommunicate a complete index of their items as well as user informationwith respect to the items to the system 100 with a request that thesystem 100 create a neural network topology specifically tailored to thecommunicated index of items. Accordingly, the request can also includeitem attribute data and item review data. The neural network topologygenerated by the system 100 can then be used by the recommendationengine 112 to provide enhanced recommendations that are finely tuned tothe enriched data provided by the merchant. This neural network topologycan be generated solely based on the merchant index of items or anexisting system 100 neural network topology can be updated based on theinformation contained in the merchant index. Therefore, such a requestmay contain identification of the merchant, the type of data, thecomplete index of the merchant's products and a request that the system100 build personalization maps such as a neural network topology basedon the data provided by the merchant. The request may also include userattribute information with respect to these products. For example,request 2814 from merchant 2804 is a request providing the name andaddress of merchant 2804, an identification of the data as relating towine, all of the wines sold or made by the merchant 2804 as well as userinformation with respect to each bottle relating to purchasesstatistics, likes or dislikes and other affinity data, and a requestthat the system 100 generate a neural network topology based on thisinformation.

Once the request is received by the merchant interface 116 via the API1801, the matrix builder 126 generates a neural network havinginternodal connections between all of the wines identified from themerchant-provided index and the user data provided in the request 2814.The system 100 may further augment the neural network using otherinformation harvested by the system 100 as described previously herein.Accordingly, the request 2810 may also include information identifyingwhether or not the merchant 2810 wants the system 100 to use informationadditional to the information provided in the request 2810.

Once the neural network is generated by the system 100, the merchant2804 can then submit further requests to the server 102 via the network120 and merchant interface 116 as previously described herein in orderto obtain recommendations or similar items generated by therecommendation engine 112. Further, the merchant 2804 may furtherrequest that the server 102 communicate the neural network topologyinformation to the merchant 2804 in response 2816 so that the merchantcan use such information internally.

Based on the information provided in the requests from the merchants,the API 2801 may capture this information such that the system 100 mayupdate the neural network topology for further use with the merchant aswell as with other users of the system 100. For example, when merchant2800 sends the request 2806 including the three bottles of wine, thesystem 100 may determine that the user picked these three wines and thatpairing information can be obtained with respect to the user and thewines themselves. Accordingly, this information can be used by thematrix builder 126 to create or update nodal links with respect to dataitems of this type. Further, the API 2801 can capture user informationin the requests thereby mapping user attribute information with specificpairings of items. The system 100 may also capture via the API 2801 theeffectiveness of the recommendations provided to the merchants in thesystem 100 responses. For example, the system 100 may send system callrequests via the API 2801 to merchants requesting that the merchantsreturn user effectiveness data with respect to recommended items such asincreased revenue based on certain recommendations and/or whethercertain customers of the merchants have purchased items based on therecommendations served by the recommendation engine 112.

Accordingly, via the API 2801 the system 100 is able to provide specificmerchants with personalization services thereby helping merchants retaincustomers and increase revenue. Merchants can request recommendationdata from the server 102 as well as specific personalization maps withrespect to information provided by the merchants relating to the entirecatalog of merchant products. Further, while providing these services tothe merchants, the system 100 simultaneously harvests merchantinformation to increase the accuracy of nodal connections in existingneural network topologies in areas relating to merchant productcatalogs. Therefore, the merchants are provided with beneficialpersonalization services while also further enhancing those services forother users and merchants alike through their interaction with thesystem 100.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications are optionally made withoutdeparting from the spirit and scope of this disclosure. Accordingly,other embodiments are within the scope of the following claims.Non-limiting examples for the above-noted embodiments include adserving, customer relationship management, fraud detection, matchmaking,real estate, predicting political affiliations, vacationrecommendations, educational/professional recommendations, health careprovider recommendations, disease diagnosis, babysitter recommendations,employment recommendations, supply chain recommendations and businessconsulting/knowledge management.

What is claimed is:
 1. A method comprising: receiving attribute data fora plurality of users, the data relating to a plurality of attributes ofa user and to at least a first venue for which the user has an affinity;receiving venue data for a plurality of venues, the venue data relatingto a plurality of attributes of the venues; receiving review data forthe plurality of venues, the review data reflecting the affinity of aplurality of reviewers for the plurality of venues; accessing a datanetwork comprising nodes corresponding at least to the plurality ofvenues and the plurality of reviewers and further comprising linksbetween said nodes, each link reflecting a strength of aninterrelationship between at least two nodes, wherein at least aplurality of the link strengths are a function of at least the reviewdata and the venue data and wherein at least a plurality of the linkstrengths are further a function of both content-based and collaborativeinterrelationships; determining, based on the link strengths and atleast one venue parameter, a plurality of recommended venues having thestrongest links to a user; generating recommendation data comprising atleast one recommended venue; and serving to a client device therecommendation data for display on a screen of the client device.